{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ui6jIzNG0g+t1AHA8cFP5fLJap2DK73iafl/e2VzfYuOkBBkfO74PNwgA2Pzjs0ycOCh1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\nnmI4QY9T/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IUGZ39UhJTP5oWVzswTxkfCZonVAPBYGamKECal379y/QWsLBMk4PkKgzFF\nug8evIcuq/PSzCLlv9U2ROCxgsWOHPFzFA9hyhJF2iAKOJUvFewvMdTFGWxBniHA5iXZ2lgszZM6\naP9gD42L1JdatYbLV8lsNY19vHTrMgAgngxw1KV+PdqbINcMcRYZEhYgZHGMOYZ5H+52cSrLbOAB\nVplIu7k2jxWGcB5uH+KHb/1k5j7VzdBlMoyxSBOCJv24C1FmYkUAyel1f5g6iEVnqavHZ7KxyzoJ\nqeDxO03zBKMxXWftwjMO0peQEFkJTVkol72yLkushHDlNZT1Aa5hrwrPZaFnabY7QIsJ4j3pYVwa\nZhY5YvYZOkwTFOxN1mg23TjxvMgJXoQQ6A/ofXkqQMB1upSUDlrLJ0Ow3Q9ajRo2N0iVOdeaR4cV\ntEVWOJ8paQV8npcanovIladclupJ7bSXAgHN50LmrpBK5PtIJ/R+olCgUafvZMYgDCnjbnSOnCF/\nDR+HHYre640msj51REoPmjMRUTVAjRWQSnnQvC4HUsErx4tS2LxI/d7Z6+L2p0QUvrhusdCiv11s\n5gifAgHz1FS0ZDEl9ttfyDRPhUdndBhaY8ziDs9TaM7RHB2nEyScoRPWuPdcbdSckXCWWdgO9at3\nsI80pnF39eotZEzu/9znX8YFNqIs9AgXuJwMRAg/nH29MdYiWKT3UvzwJ/B+9+8BAJL/7p9h9VPK\nUu3+n3+E6y8+BwC4Gc3jvTdZYHB5E0t/8Cv8EDJkPD90XiDg2remsGUlLkAZ9B4SlDkZ5Fh//gaA\nsowUC8JMgSylvbCIE4DLe+HaKio8n2JvWjpqljYYxBgxqX1uYQ4xP38fERJ+nmHgu708QYF+Wtbz\nVGj7tJa/f/82TE7fbwRtDHn/GYoCfXedCAPQPfeHNUcP+YvvfBdffO3LAIC1jXUcHFJ2b9jv4doV\nFoetrcNbpX/30gKN6uxCgs/0MFWc5QLYKa6fZhpFOQiEdDCclNbxMYyB0/BZqx1PCtZAlgk2AefY\nptR0cBitnRLJiql0MM81GemB+CNbnNJWySFWW7TBVcKz2sEnt81Llx0/ZCvOcMCy5E2RuVQ6fA81\nrullZQHNhxOhFGSVD4ZRBZaLZNrRBPmIIZAUkLq0hTAIuL8hDGp84GqJAiMubjqyFmNT1s4SuMIG\noeuTEd5a3uBnGMHOCmX6p85heDjKMZ7w3wUF4pwWpTT3ICzfu/Bh2Mld6gQpWwVMTjS6h/Rsdh+N\ncOnKFQDAzWdvYH2NaiwtLiTY6hHOrlWBICjlugE8rqfW2drD3UP6DGGx9gwtIM+/tgqbkbkedANp\nPPvEl0JOx5qQjlukhEXIkJMVBpY3Vd9XmJ9nE8jVW4hqNHk//vknuH6DnvF4kOK5Z+lg9cabP8XF\ny7SIHQ497B/TO7l0axEebxwTo1CGDydDBXFCcN7SUg0pK6niYeZgzcV2BUtLs7suz+kuTFnf0liM\nSuO/inQFRlUgkbOKCeOJS6mLooAtLUWsceltTwBgCMcMD2B4zC5eewYqKg8gCpYh2izLoDXDi3nm\nZpnnefAqZQ1J64qj+551ZouztM7oGAAdtG3VAwo+tGYFcr7PLE0wOaDPwenAybc3Ll5zppPSm9br\niocDjPol/84iYvjqrGzfVxJ9Huef7h5ja4+Cg92j05KaRsaeKCXtarpWWe14c09qh50UQ4bW42SM\nIiUo8vLSHFpcF68wGiErF6t+4Lg/o+EQKSsdK9UQKz4dBIrCIma4OAwD5KxmqvgVZy1Qa0Wo8mHh\npJ9gi+GVUZJhVNKhlESXLSfMXgdxQlQGJeawMDd7bb7bH9/G2irNoVolcOrrIAjPqEunRcEFcCZA\nlmgvstnxhXVMhnRzh3uHTu3YrFfQbDGXqnuKTof2gMJXANf/fP+Hb6F5iaDg9UsvY75G9/PFV2/h\nT779PwMA9nZ3cXrCa9LpECvr6zP3UckAZoM2c4Shqx/Xb9fRYRuf0d2f4/DOWwCA614DX1HEWZPf\n+D3c2+EarukAg5jGcu+9Hdx8hdbR8cNdnI5onHzu17+Ew/+BnOD1SYHWf/1P6TuXapiwqbRpVuDx\n2G/2EizvsHq83sZOjQODRgE14zgFgB+++RZO+zQ+K2EVghMCXkVA8BrgS4FqxEFXmqJgaku10XZF\nuReaVUSCxtLVZ65h0qP3tbG6BMvmsREkNBPGqgrONSCZpHjj+z+gfrXbULyftJoNV8UhCEK0anR9\n3Wxhf687cx/PYb7zdt7O23k7b+ftvJ23X6J9ppmpcW4d6VxAu3p8QqqpXXyWw3JZbqGmygxhjSOo\naqGmdfoEpoaABVUrBwhWU0yYVGJakoBs884oHjhtabXGHtdruhUdYYHLgCjlIQhmP3O22/MomLS7\nt7CELY74V+IeQsMncBXAlGZyUjsyrz85geG0Zd5ehbz5JQCANxnCv081oOJeH3wZhJMJTFoSHSWq\nfJ8+AMWKkAAWFSaJD3KBeEzP59nkGCnXPvpofc6VYHtSSwqD7j7dYxIXiFgVE/oeCjYNNQYIQjY9\nlRbjEUVsvUGO0SF9p7MXY8D18uTdbQQ/JaLwpc2LuP4MldM46eyjvk6/tbDeRrPJ46LQODygKOrh\n7W3ojN7VxrVFtNsUZd7/dB/jIf1uu3kBtXD2rA2EmsLLQjsoJ1QK6+tECi+UQH9Ez35xroX792js\ntFtVKEkv6Guvv4B3fkzQ29zGEppVIsN6UdPBc5kWaDRoLISBciaDc3PzGDF8meZHqLNKLgqrUBxp\nZVmCTp/9fsIKCSpmbBWRImWfLwsg5OsHjSo89kmLPIMJk+OHowS6hOGscYRNY6bQi2fh1KJF7wB1\nj6LJxaqE5WtKaSFYMDC2OUZMPpVFBsv98hAg5HpjvvIRsteV5wnIdHYlWL+2joJhyn5wCsNQaYgM\nfomhe8LVEGxUQzxzhaL5S5cuYnefMkqdwQQVzhYsrS6hX6V5c9A5QcH3HFSrTn2UqwpyphnsnJ7g\neESQRqEETGmSeKbGohUalj3UtMlQzGigu3c8Bos5oRSQjShjlsQTrGzQOD0dj7F1SNH1eByjUaes\naZJOUKvRHO30OihKsm9B0Tl9Z+xUcsk4h2Ul4NgajFmt2GwvIdM07u58+A7WNi8DAJ59/nk8+vO/\nBgCYYYqQswC1/gT5U5Cz/+iP/xdsbpJBb+j5uHKJyrTcuvEi2i2aT1KdMSoUcNldYadmqydH+8hH\nJQqh0DkmMVD3pMDqJmWabOCj2qD1Q1YiHIwJDsMIaE54jEuDB1ukWLzzv/41hkxJSCYGGcPXhdY4\nPhzM3Ecvz1AwCV5MYvz4v/pvAACTpIebvF+2AoP7fc6mJYd4vsr+gkf3cPjPWHHpVzE/z/SRv/oA\nlQ8p23W9c4yPEob0ux1c/YCy/ZcWngW+T58f/NYXsPktUhVnc014Dcp86Z8fYp/Vl7ff/RRjNmV9\n7p+8DtWYfb2589HHuHKV1vViMMDNC/S5P5qgeYmyh5lOMWAl8cXaGrIx9bfSmkeVx4+ejNCscNk0\nbXH99a8BAE77PSd4WFxexKigdSXdP4TgupPd4w6GE5owv/rr13DlOo2lNE3QbBPMmmUxHj14CAA4\nuv8Qc1/8wsx9/EwPU0lq4fgPUuB4RAP93s4hdrrUSa0NGmzI2K4qVOo0yBqRjxo7wkWBAX8FvgDV\nGwKQW+NkllJ7sFyHR2MqcyYZ8hR3Lw9ZkyTHvR1KV6+ECSoBL4wGrsjiLC0MQ3i8uaxuXEDG2G1+\n5xAsFCGOCR/0TK2NlO/By4fIUlp4QzuBufs+AKDYeB7mN/4j6u9kguE7fwUA6HY7CNmJuJ6kKCVl\nPiya7JgdFQWiEombGBxynbCTSYElxpIfCQuZz2ZO1u9kKHQJA1VcGn0QdxHzwc73fficjj/Y28OI\nc/9BUUEWlypM44ouC6VQ8IHvwdY2trlmmNYZlg7oO82tHhZWSPq6sFDF4JgWq+b8HG48SxDhjRfW\noZhz9s67I8y3aUNZX7qEyG/N1D8AUNI4SNFqCV+VfJ+ps7UXhPjNX6PCmsZobO3TRrZabeOFa8Sj\nWKhL1Cv0jPdPdlC/QItPqx5ieZ4WhPc++hTNKvWxHgUYsa1CgTry8n16A1xaZ3VpoDAeUt/7vQF6\nXKuvWrPTwsIztKVLz6Db47EWBGBAFFpbZwsiJkc4uEucjaOTPtdVA4IggMdp8Uql5TgnnmeddFpE\nEkM2nS0mA4gK9dFK4xR5Jp9A53QYtEajFOoZmcMwd8KKACYvLQeM46vM0jpeDdqn380aC6hwwJAP\nPGQTdvlOE6jyt4xGhRcWWyQY9mk9GA4mqPHh9/LFKwgjGm9bBwc4Kd+XNhAMpysLHPNmPZr0MYrp\nHWljoPgw5UkFwzQHqtnHh9MihzWz8cJy4yEvAzHPw+YmbVBJYR2k0p3ESPl3tncOMdek4Gf9wpIr\nHJ6kI1TYwb8S1TEa0RzK0gINLnS8t3OEOtuUzM3PI2DT4ZPTCfaP6Fn2hhNca9C4frR76ODiMKqj\nwu8/y82UGjBDu/3uT7G7RcVm19avQAgeazLAC88Sh6jdnnsM5huMqe/7h9u494CC0CLLUeV6jDAW\nz918FgAwGPRwsEeqrvn1ZccjvHf/Pvo8P0zgYXhCfTzefYiDAy6AnaWo8uG0FilUuAZqbgo0m09R\nzNkaWIbMgusXEf+A+vJ+fQXqgMx9n9U5FjjwHwM44Aoa8tPbeI5hsn7UxkTReGinIxR36ECRD0/x\nMl+/86cTWOYAzycjZG9/DABY6uZI7tDhsTtMcZ8PiVvdB+iAnmc9HuG5l0kNV82/AGFnTzL0O6eY\n573iq8/ewjJz8T7efoBqwpDx3iMcdKhfTc+HxwerofQQ8Ua93GjglPe2jvThM4/v6LSLuQsErfYq\nHvo9drX3PHz+t/4+ACD0AvRiuv7uoy03F1tzLUz4t7TOHddMeAJGzmZmDZzDfOftvJ2383beztt5\nO2+/VPtsCejQzuzv/uEI/+OfkoX+p1vHiLPSeDFANWAPoVBDhpR2rQYRGlW63Xo9wHxE58DFes1F\nTLVAIOKMUsW3CJnUTkTFsx5VdD8SFgGnLfc6E5yyX8pG00diKSOW5tNs1yzNk8rBlGGzgc6Y0qXH\nkAhZneBZA3FaSvUi6DZlTf75ozEexvS7/8VVD5eYPKv3PgR6FA0NvvQ76P5n/y0AYJyOcPc2pWkH\n772NW4cENd042MZ8QhF/3cIpARPho8pZhE4xQu36CwCAKxc3cftHfzJT/05HJ/Ajgm+UqeGwQ89M\n24mLbrWR6JSR3GEH7TpFt+1aBfOSvtMQOXpD6t9pfwDDkZNUvns/UVDB8S5FGEYGqHLJljgdoTVP\n17l47Raac3T9g+4udj9gH5+shZWrlJlabLUwHMz+Dl+6vooal9SoVXwX9e6ejJEa+vfLlzbwpS+8\nCACoVCr4P/6YKtJfvbqKF14kWOJ4bxtf/ir50Nw/vI/hPr2Taq2CY66/pfMEVc64xuMhjo6pLysX\nmkhP2ZBzkOLyqxR1dfpdFwEZXWDQpyzCeJI85rP2pLZw+SYaDEtUa1WEbFxZZDmEpffy0Zt/jP37\n5Ls0irPHvNtQRtKVJtbXCSZZWV3FJKNoeG1tGWGfrjm/vIb5Tcrm6CJFkZaZj4xKRoGEITGXRqpU\nK9MafIbqLAKAUgojhm5naXJ+HlVeGxpLLdQ59R+f7GBwzGrXzhHyPmcg8hgJk1iTKMDxPmUsNCRs\nSu8li8eYa1OG4/LFDeRsDLu/f4iUIVHPCuhK6Y2lIB1R17pK9QISmkm42hbOGFhKQIkzz/nf0tI0\ncUIJZS2aDco6HXRG6PFaU23UXPZ9YWHZmTSGUQ2CFbFhLXBZNWMLhEwuj6IQJzyPc6SuxJaVAsKj\n57q1cw9GUnbglS99CQ+3aD14uHUKwduLBByxv+JpBGr2zNTB/inV7gSQpVOPqvGoA8mlhTZWNzAc\nEhH5+GQf77xLRO1H259gcErvdqV2Gct1WouvLSxiOKFszieFhlqhubVzeICfs8dTnCROVRlnhTM1\n3dt9hHhSii8k+hmv6SFQ1lrxowAV/9rMfYTy4TGEiptXnCpzlCjc8emea/kYN2q0F86nGZZYff3d\nLEMLNGab/QxV3i/XFjdxHDKB3otwg/ez5ijAiaAM0Y96Pex0qYbg3fe+h/2MzYaRwuc9by6UWOIs\neuX6c1j4D38DAKAbVVfrcpYmtcbtd35KfTk+wiqLE0a9Y4xYjFWv+rjJnovt4RBI2Wg7z1FjmN0M\nB1BsyKmiCnTM8HFeoP+Q3l1U8bDIyEgGhU9vU3ZyZWEeYBXyZNjHwR6JerTOsMnwfhRFCHkfq7ca\ngJ3M3MfP9jBVFMjYAfj7P76NvfuUvo0gMGRuw0gUCHnyt1QHIqAHneQtdHlxPh4o3OPUvwZQMGYf\n+go1Tl03qx7a/Lle9dHmw1c9FIh4ofY9iSpzOT54cILtMX2+1F5yppOjOIMuZt+IlZKQzJfQSY7b\npzRAh7aKrzBc9IxJ3GD1khhHp/T9NwYF5l4h6ea7/hBrfToYeJGC5Y3Pf+fPUFukWkPzt16DatLE\neG9lE3aeFtPO7j2M/+hfAQCqd27jtCxiGlSxww7hw8EIawzDrG7t4ZGZDf9O/T5EQPdeb9Rw8QId\nWKoNg10+LOztxhiwJYTyJBa4MPKC30Cd5cl6XuHTOw8BALVgWnRSGyB1QrEC7TU2rVsWqNTpHVbq\nDdy8SRu4V685s8q9gw52dohLtdiqw+ZlodUYOwwd4stP7uNrn7+JZXZ1btUrOOEF+fD7b6PO0GSr\nHuC//5/+JQBgfXkeK2t0P9IoctkH0BuM8PqrxHuzyuDd3Q8AAJMsw7f/kvgkv/WNl/GgR5O60wWe\neY5UQyvzDfS54OznX3wGtYgdiY2P0YTr4sGiXqEx1Rnkj9W/e1LzpGX5HeBLQDJ8AuHDslVHnMRY\n5oPqXCNCztLmoiiQlMaLKsHxPi1io94OFhcJMFStDeTs9tfPBF54hg7u8WSAnA0lR+ORO2hYWIxG\nLHluNpySzhiLgk/Xvh+guTZ7H6v1KuoMCajOAP0OwXZFtwuPAy1PSBh2MV9b2sAqu4Jfu7iBJnOK\ntnd2nR2CyRPkLJ+fpAmSMY35IomRs/Fprd7EAhfSTbIEurSD8QIcnNBYytPCcd+UEY4/CmFhZwze\nqpGPGgcq2WiAIasMO2aAly8QBFarhShygipq1Qj37lPAlesxLl+jvq6sqG6VpQAAIABJREFUrjgo\ndTyMIbgOaDxJscpy//UrFxGPqd/7Jx2kPOf8Sg2tFr3z5fWL+OHb/zfdw2mMJhdnz9MEYzZanFtp\nIpgdjcYkiZEwT67fH2A4pHeYxH3cf0gH/byYYHeH5vdw1HeHryLXiLi+W31jBRlz5hpraxA8futR\nxdVf29rZBxhKq1Qqzig3K3IUfJAMowrGht55GPjOhiEeJfDLIDoMEarZJfV/8d4xHmzRXH9ZW0DR\n2LnW72GRx++JUFCSDhF3wxa+zfYA7/d2kfM4WlQ+/pGgefM+erhn6JkPTIE3JgxpdQ6wx7BgJxsC\nZTFhX6DFiYWmX4Fq0x5zvLSGHu+jV772GsJvfhUAoKV+PLh6QvOEcPUtO9vbuMh0k40iR1DWmrUS\nogzqhAGfk1BrNBGwnZHME0wsva/AV0iYkpD41tX8rOcac+yMPrA+DL9TXwm0ahyQFzli5mQRtF7a\nxABtnruNyMOD8fHMfTyH+c7beTtv5+28nbfzdt5+ifaZZqZ0YfDuPYos3rvfoTpBACoiQUVQFFjk\n0zIjdwcKYUhRVRRNEHD1+ND3XUnscWHKwzVGwsMJ102C9B1x2JMSzQpX1lbGEWkrfoAaJ2T2T0fw\nF4jdf9cM8DnOuuYidxH5bM1C8Sm6Eng4YYXBTxKDhxxt/6YQeNmj6KAO4JCz3u3VVWyuUJS3lQTo\ndCgztaIVshXKfETjAWrf/hcAgOT9t7CwSCnqV8Ma6jlFLnMvfR6PtgiiuPvehxgwYbzIx/DZQLCq\nPOB9SvGOhUDtpVsz9c7zQgjOYkWRh7XLlOI/7B6jzyf9OE4QMkG1vVJDs0HZsHp1Hs0qEamlkTCG\nsjC+irDWKlWBAY45sny4dx8Zc0Z1mKNkKOeZQG/Mtb5Ci/GEo5PEolqhtPili9dRZqG7/S6G49n9\nQr7wpa/hyrNE5s0mQ3z8MaWJFxbauMp+WL3hBA32kzrpTvD3f5fI6Bsr887oZhQnePQJiQh++uaP\noXkcPf/MVaQcbeu4jyor6dqrm9jgTIC0PWQFG+SNRuiz8jEIJZSZ+jS1GY7sjXOov1Fk429vzUbd\nlU8CLDQrO1UQQrDJqjkDG1YqgfPjURIwGd2/8CSGTN7sj4Ccl5S5S89j4VnqS3VuHaXvrYqqyEoC\nelaQ7xtAZpxcpkMLH5kpzX0lMl2WbzHOlHeWVhUaKZs29h8+RJ8VsWp8iqj0nzKFM4V89rlb+OKL\nREzOJ300+f1eWF93ApZWo46c7+f04SlOjykTenx8jLkmje3NixfQnuMscbfrysZUGgEmKV1nkp66\nMjmess6E0lgNO6MxaaUaoMHqs5PBEIqNP69tXsKwR1mJ45NjXGAIo9P7FO0mvWdpPUDT8w6COurt\nEpYcYjymcRpUBbyInsFkbKDZO26cSUAR9GNgsbVDa8q//vM/w4gzPs1GA5FHfyvUtDZjVmjgKWC+\ntfYcch6PUgr4PHb6h1s45Gx9XliMeT3wvAgR7yWZzWF44J2Ox1hrUb/2DrZdDndYjHH7DpGw4zhB\nhZWMeZEhZ1WlsRY5p8tvLl/G73yTxshCreroKbtHh5CsNg+ERGcwm6AHAA60h/EB7Q3bb3yMGwOa\nWx+bPXzAWaSJKTAuSIDjewGiiN+jSRHzHjM2Bv8ioQzd4GEBwxAkdOrKyfgqBAvV0Qjq8ENaYHV9\nDicsEvjAAkP2L1yzKdqMtCxfXoHk+ykyDVtC8TM0FfkOjVHSwHDGO5J1qCPaq4q4ALjEUb9IEPF4\n8xo1pCwM0TsxqvxZtKsYsrcXrIecvez6hYZJuORaGGHIBqFJJUTCysRxmqDg80R/MEIypjFcr1dh\neE7V5ubQ+8nRzH38TA9T2ycT/OGb9wAAH+3GKBLmCeQahaEUtbKnGPiccs4zlL5soQrcQuqFCoIP\nSr4XkWMy2EVZsDGfCpFz2lJJiYK5SFYYJ3WX0nep2VrFx9IcPUSbCtxn7H91ro0Cs0/+LEngsxLM\nUwKbG0vunndZBv4n3R62GWL7Fb8GzWnIKKg4V+SerOKIpeVrnS2o8sQYtVHngkr1d78Hw3yoea/p\nuFqoN7CyQwO04lkkvP9kKkTMB7pOtYZjhmT24wQTMRt8Ios6hCgNAT2c9GizGg4SSJQFJUN4zDdo\nt+toNunQ1GrNY47rlFVzjRXmCoWFwgo/+0olwinzVsLFLrbYpPGgM0aa0HtoynmEFYaBhIEf0sY4\nPwc0fLqOzhLEY+JRSAlEwezwUKZzbKzToSkvEty5S5Lhl1+8hYA3fGsN/r1f+woA4PUvfwEJc1QE\npvYGm5cuY/cuLdQ/+OufIgrouf36b3wTinkCc1GOimR+RTCHXpcm/tHeHg6O2b3ZBw5P6KAaeBq2\nVEl5Pro9ela1ikCSzehvAaDWnkdUcgohUGA6hwI2c/SUh8KWNc8UwhptoFGggJjGsjIJMt4ow8Yy\nXnz97wIAPveNv+f4jmmaomAoxfcDCFbRhJWKc5SXSqHVZvsKIR5zOq+zukbrp4MW8vEAunRnr9ZQ\n44BE9nwUXdqY8iyGYIPbwaiHgE04VxYuYX15jZ+PwnjEUutJD7us/mpXq7jKTufNag2bXJdueWUN\nO7ukjFpcWkN/RIqsfn8MxRuTgITgeR94HgwfhPIihp2xNl+WxhiP2VpCCae8U8Jz7ubVWt0Zda5v\nrsOu0jw4Ou7jwRbd497xCV54kQJJKWoY8rsVwsPWfYI5tre68BlSXlxsYjJmc8jTCX7+ET2P/hiI\nauyYLyx8VllHvkCzVtb7CxEFs287KrEIytp8GtCndM14KCH80sQXmCuLuefWFf6Fr5xRcj46xSNa\n0mGb845WcPdwFznz/Jbb1SncowNYdo4/HsXo8ybspwVChnahBE74oHf38Agxw/KiMEiy2QPwZ5Ya\nuLzAG/vVdewdUxCylu4hKN3ZjXS1BVvWR8PQnPBUDl8y7AWBmAP5wm+giCiwnCiFIR+Kk7COhNfd\nR0ZjzIFBUsQYTthawxqE7lDp4Qt/hxzWn/3iy8hLta70z9QjfXLzqxWocrynKU7YCmJUFFhhDm4s\nUhQMuadBCMHUn84oQcYH8xWt0MjLAuEBVM7ZEOPj8peJUtEbdhE/orE91hY5Pze/GuLWzZsAgHvb\n27AMkVsNdJkCoNMMY+Zujo2HvZPOzH08h/nO23k7b+ftvJ2383befon2mWamfnr7Ad772XsAAF0U\nUFz+oMgyFz0ZpWFqdBq3SY6U07qJmKqqIARVVwdllLKyxp/xIDmbI+A5tYcSBiEXhJISKBM4ke/B\ncNSLSeA8cpp2iIMtSpd6yTIK+xREO08+Vofs0gWKXC9d3EDK0eLBwQF+/IBIx6eFRo1P+CLwnWHl\nw+1tfLpLfXm+0PCOiDhqsQWlGPuywpX18DBxNeqyVDtISYcKJ5au8+GzXwE2y7SuD7NAmSn9aBf9\nw93Z+qcaCDnK0UWAPQq6cfHqPEKOKib9ENKyIqIWocbqi2Ayhj+gd94wCvWQoJBMFfBYQZbnGjET\nPEfBGMMh9SM3voM0FtdXUBb4GscjTHqUii1GfSBh76yDB6hEdP3F+Tn43uwp6Tfe/DFe/xWKxpZX\n17HG5W2a1Spuf0gK1DRJ8Zdv/hgAUKsCEUN1uwcjfOk1ipAqgY/lVYI4f/+1q/jOJxS51lSGS2tE\ncpxfWMDHFNhjoRiiynBbraqch9RcQ2DA6qM0TtGcY2ivP0adjfOKsUXuzW6iJ4TEtESlQMDRvCeB\nQybznp520Jqn6DasN3H9JfKY2Ts4RP+AbjrMjpBxdulXf/sf4tVv/iYAwAQhsryMYuW0viGAOtcc\nbDQaToF49r9n2VQ5KISY1tJ8yjYpFAQrzSoLa6gziTxXwDBl8mk2hmIl1fHeHh5+coeew8WLyOqU\nKRtPMmey2l5dRoMj6TwpCC4DcHHjMiqsptve3UOv2+f793ByQhnSk9MugjIzG/ooeO4WpoAqUyVq\n9gh3fq7t/HEWV1rO7PPhdgdLayRMqQQRuqdElWg029jZZUjF5vC4bMa9h128/e5fAACazSb6XXo2\nrVYDQ65JuL1zite+QhnUV774In70BpG/tx/uImUfrcALoRjSrAYSzZCyHhdWW7iwRu98eb4CX82e\n0vjo4e60DuuZOnpRFGBxjRVeRqPDXIk8N25cS1h4jGCEkYc7nK14VwbOgHYYTxAx4V4pBcMZUast\n2m26/oWLq9jeobTWX77zPj5i4VRjcxnHA8pq4ajnvPV8JTF70SPg2aU6Ptmg332l8RK6mp75tXer\nCHL63WqgoCTdp6zUEXK5oKQS4IiJ5rtZjmPQ8+kjQIezbH2dUvoFQJolGOdlLdM+Ci4XJa0T6EJY\nDcmmrLde/wa++M1vAAAqfoQSKhJKOprOLK0oiNwNAJlQ6DP15CDXmAQ0DsdQ6Hao7xXlY6NOsHnl\n6iWYqBSqnCJlNbD1mqgs0hydW7+IXYZct46P8Xe/8jr9VhTizaP98i4w5OzhvU8/QbhMc6Rx46bb\nO7WOEDElSGiL487sBPTP9DA1GRxC9mgzClUAVcqirUFewgkaMFxfSOUGYVmMFcV0gdUWgtO6OrEo\nym40lrGwQBtuwxPQbEQZeMBSkzb3PB5hyMVzK1JCsFpCpxnYcw3NZg3zc6Tm0vDQ5/o/s7R6vQZZ\nOvBCOhjDAoj4ENK4VsPSIm1SB7sHeMCbV7XZdE6s9X4P90CTeVAozJ/hsVjLElYrIXjCWC0ddn5a\nGBwV9LlXKDyI6Jl88vyXsLnEm/LxMUYf00lIbD9CPKPrcq3ShmSYxhQSyZA+d/bgDPUCFcA3zMEY\nJtBDhgL7/ak5XXMBGcvH0yJHXLoHe8BeTovDqTbQbEXQbFaxuMKDv9VEWbcuKAIoQc/SqjkYyQoi\nnaOzQ89Jj2I0GrNDtd/7/jv493+XXI5PO10oduM+ODpBf0T3ubx2GZf+P/beLFi2LL3v+q21p5zz\nzOeeO9etuUrVs6RWS3Lbag0It8M22JYgjASBCQhCCiIIggeeeSB44AUpgBcTBks4CBkwWKDJkiUk\nq9Wl6m5V13yr6s73zCfnzD2uxcP69s5zW9W6eVWKCiK0/w9VWacy97iG7/v/v+HpZwH41V//HX76\nb/2Iu85Oyu0PXHbbWhN2RLna3enw1Njdy5e//zm+9nUXS9Vf79NrueM3ilOSxN37vcNj3rvlFtIv\nvLTJwbEbs+sdn/Gpm/hPXdlinpYSVcrZYPWqy8AjpRQa4ng8vHeL3/qNXwfg4uUrpF0p1HjxGj/w\nY3/TPZNpwtkDN3a+8Zu/xOXLbsx+6ktfIYv6cuy0ktA9z6vmrjGmMo601tXfrbVVZp/neY/IfOV1\n+r7/iNH1OORRl0w2hajp4cuYmc0mTLvuWWUmJ8wlzf90yjded7JshM/xoVtI33znXa5evQbAj3zp\ni/hScFfjk0j6dtDwuXPLOTy37t5lLs7M/tFJVcAzjmPEB2R3Y6PKBJtNpuTy/UQVBGo1YyNZzJlI\nrMf2ZpeRyE/zOKGXl3F1hqYUqzw6GmGlpEGz2SaO3ef9wwPeetcZGoqHhDLefXVAU+SY0A8IZV0b\nD4bMJT5yPltQSAiCtRmecWvx9nqXF687h+HiTp++NDpuhDxRCQ+lPFTlnHpYWeOSJGc+kVCCzYDr\nz7rjT6c5USTjSxlGkgGu0ZRtJkfTBalsnnlRVEa/HwTYMp7PGmIxiK9s9OnKpn1y94hhaQQvFnjy\nW600h1K2wypdlcBYBd93pcNViX28lAfcuuoy5t741AX2pWyH7TSR1oIcTsa0N6WJMQotHQWGScaH\nD5yTHngNDiT26vhkjJo5ucokYEujjAVBaUAZyFP3na21Dl/+inMm/9rf+NGq3JBazEE6W2idVbGb\nqyBstMg9iYFCsSn9FpOgQTpw+9+7t96uCr22PZ8tKbly+ZXPMi5K8/QB4YZ0R+h02eq7EBLT7/Pb\nX/sdAO4OBvzUiy6u7Xg6Jhu4ddSjIBGndDIakIh/nSULWmJArfW73LtzG4DFbFnuZxXUMl+NGjVq\n1KhRo8bHwCfKTF2/sMGzO85+G5yd4ksAofXMsiM9S0lAN5atX4xVy4yXwoAwHxboiDRi/SHtWAJ1\nVVZ5wM8+/zkKsaL3332fbOAkLRN2mWXSnZ4Fly842Wtj6xl6G47WL3LQ2eoWeK/XrYIbFarywqwL\nj3efjU9jx3lS6+vr3F9z8kCcarLMeVI7Fy5zKJkWE6XZEW+ryAqMsE4UlvvS7+3wxc8xEy/pzr/6\nI96RwOS7uSETGSN865vEsRTBfPCQTFiNbL2JkUKEj4PvuQJ+gOvnZRwTNDwOWeuLx2B90oHUhjka\nokumscjIxNNNswVJJt6wH9GU4oq0FJOgZJFC1vvu3Xb6bdpStDPLFsRzKUoadTHa/T1ROVZYs+52\nAx05D7IRKoxdXVo4ORvxD//HXwHgp37qb9HruuNrz6cjQYuv/vHX+am/93cB+Pmf+wd8+1uOafob\nX/0qfyweUjcoaIrX3mr4NGMX9GyTGVrYqHv3bxPkjrnr9xRz+b7S68xzF/ie57aqedTuaBpSxHJn\no8tvvuoYoryAqztP0MJC60cYgpIxeeP119m96Hp6vfLcVf7wd5388+ynfgDdcvOjHSk2pdbZvZvf\npBBvNeysMxeZJ1S6Sh75TqmuPO93BpOXUr+S6yu/U35WSj0RqxH0I5C2HnlekCaSONFqE150LX/8\n+Yhi4ObE5PA+d1LJdr27Tyj1bIbTE2a33Lqyu7NBJEkIH+4/5O6h83oHkwknwmRM5rNKCsfYcwVI\nC5qy5nUbPn1p7eLvblZZRvsP91nrrPYeiyKjEUkW2P1jlHHz7OKlbTzJWD46GtCW43lhhJZA9/0H\nQ26+77KFDw6GtDtl70qFFlbL5DE3rrqxsLcesCZy2Gtf+xZCTLGxsU5/QzJr+02UrEEvP7vNM5fd\n/e1u9inKvn7zIT2pO7cK8tyidZkwpKterUUBo6GsK55i75I846ZHvHBzPS0yAgndCH2PPCiDs33K\nIs6Riip5Ky+KinVvBi3X5geXPScRBpxYSGUlb3V6HIzc+9f5ss7bPFmgk9UZ1Kd6EVek36Onwe65\nuZU+3Wc6FfXG8xhJyEN0+z5rbXedG60GV7fc+11rBBxKG6H3c59fft0xTSdf/xBzz2WyMt3Hzhx7\nZSYnpGqZ/a6k7lzvyjN4V11duPfHiql269OV9QaBtF5Syls5UQJgbX2TqSQWba5t8PzLn3HXtkgZ\nbbuHO96/QxCXPTwVey+5WmmNrR2UqFiXty/T6suemlmsKBezZsiphNGcJhn/UtbjSTxlKskv2veJ\nRebzbFH1EfWV4vaHbi68f/NmVRPt5s2bhBKisgo+UWPq/fdeY33NTaR4cUy6cItPMoeijJ3QBk9G\nt6+Wk8fqc4uwtRQiz+WFrkoReItjTvZlM1IFDel5tnnxRR7G7ryHx+BL8bmWn+CJXru926uKe92+\nfYe1M7fRh2Gbk9nqjTmbzVbVJFfDOWOK5b1YU20kfhDy7DNOLto/PmX/wJ33+HTAWHL7Hy48tstU\n2EaDZtmzrSgopLHow+/7AkYW0GJ4yvbvu8roujDEkhqa/dFvVwVRH/qKfelr1Gs16YarDZo4ztAN\nuXbfkMeS7qqgWfYdSz2yhfSpmoYksrgFWpOLDDCPUzLJzgt7mplsODaEzC8XyYJ2z73DsBmyiN1k\nHB4MOTl0C4WPXxWZ9HREv++khfX1NdavifadGQYHq1eytdpy+66Lw3r1tW/TKntu5TG+3OOXvvdl\n9o+c/Pv6m2e8876Tr+8d7LMrzRDbV9okMk7H0xhkYs7iGVd67jncPpiQ5CJlrm9XcR2D4QkbYmAa\nYypN39N9Iimid/doyD1JDV5khutPYEwppcgkayWKGkwklk37AV/8ISdZFvMx4brbTFV7i7kY8XGS\nMp0N5e8b5JISjjWEuswQtFUDZK2Wct53i3+y1j4SP1XFyRjzRNLeI/AsqqyE7HmkkmVpWh1U5pwl\n8hTW3VjqrG3THLnnP1ocMpY4wkbLw0oxwT+5+S5GJPe7D+5zKv3b5knGQhwhtF/1H9RQFedsdtuV\nobTZbfLc00467HQ6fHjLGcWT4YArEmf5OPR7PY6HEv8yLeiKwY0PRydu/N6/t08g8soLLz/P/pG7\n13v3xpxKhii2wEgl9zBsVtKP9gI2+26cfvHTe4ykY8HbH44wkpXYbDXY2HIO3cXLa9x/3z2zta5H\nS9aJdickl8LBXtihI87JKti6GCJ7ISa35GVMk4FmWDZbhOGRhAkYi5EyAGlmqgK6CQVpWo4vF8cH\nLkO025DQgCyrwhAaURMrsUWep/Dlt37gV30jvaxgJoVa5+Np1d0DFE9QpYRALTP1sIprDenteqVZ\nhU4oLGkZc/TpNdpSHiAMPAKRQYfzlLP77vuvvXqHu992RYKTb/5z9LHLolfpCCWFLtE+nsTw+dpg\nJQP7cP82r/6+cwg/H3+GnZfd/mQ6wTJmKgwpwtVjNKNWxEh6JtrQpy3Fj6MOZJLJbwFfnmGkYCyG\nz2Z2iUgM1elswtlDN7ZbmSKTNSnrd5nfc87qYp7y+6+6autRI6Cz48IQZtrilSVp8pxC3t29u3cZ\nvSOhP41mRfJ8ePsOL7348sr3WMt8NWrUqFGjRo0aHwOfKDN1fPIqrY7zzq8/tUURO4t0kcCHd6Ve\nTsPSlJokw6EhkwBPZTWxZK4oH6QOJJOhIROvp9A5RRnEqn0mY+d53b35JlPfeaKB79Falwwljmj1\nxBJuZ8SJC4SbLzKOB9K3qtHEs6tnggVBUAXPKqVQ5+zVpdRkK/YqL0zlffQ6babC6PQ6Td6YOEv+\nvxl7XDh1v+37lqekR+HzUcDWfedJP/Nrv8W+ZNON7u1zKgXM0oZmIt7EoRcwKLMaOy22Nx21v9YK\nK1bucSiMxZMMJh+NFRrd5gmjYwmMn/nEg7LAZo6V95MpVdVxSbRFSbCh8bwyrhEbxsQSYO8rgxIJ\nbzqdcihM0IN7+4yOpb5OblmTpIMLl3eJ+vL+u5q5BLUvBjNGg9V7uhkDM2kZ8k//2a9RCPv20ovX\n2dpzUtdXf/SH+PqrzvN77f3X+Nmf+WkAzu6N+IVf+J8A+Pd+7Gmu7rmxlkY9djedF9jZ3qMrmSr3\nT29hQjdOC71gIXLPMF7QDqVYoefTEp1hb2eNTOpAvXsy5rJQ/LfuD6rCnqsgz/OqfUuj0eTo2DEW\nF69cI2q7azNhg862y2R87/ZDXuq5pIx333wTIzXTLt14kdv3RI5MY3zp62aUqlpznMd3skwfxUbJ\nN6t/V9KeUii1uv+n0rCafUrlCGGBtRYhD2kXhovbzkv+9I3neSpx7+vha7/H2wduvRmPDSNpnzK4\nf1S1jTHWkpXjOcmq+l/W0xQimXieIhINdaPps9Vz83I9suRj52GfDI85eugYneHZKccnq2UQLSYJ\nWrKi+s02SBbY5OyEnV0XmPvA5JycOQ//9GidrvQ+C+wZu5IRNp1b9oXh0tbSbooUGUSshWUhygVK\ngoCzJOVg6OboPLEYYcrj+QndUFqSdDSqXO/yjLLWahQF+P7q7/ArP3YBI61HFrOC+dStJcdHSRW6\nEfoes1kZaO6TS2FJU5iKFacoUCJxam/JHGLzql+hp33KVMBGo4EvsqYfJ2jpgbm9vYknUnA6GNEV\nWTvx/KoQr9JeFZ6yKsritUqBlgDxpgZTRnRgaMr9RkZjRNJN5pYTKb77L964xa9+y2Ua3rr/kKtj\nNy+fvpITXncMcxQ9jZRSI/QDIlE8Qu3TlM/dfsjWppsHF/cCmg23D3lZAxuVdRwt/oq1CQEeHNwh\nlnkzGDe5d9ddWzIe8a333pD7KtjpSWHgbMHg0O1t/V4fX+a9CjyMaMyzScKpZJsGwx5dsQ8iFLOZ\nMFCFQWq10l1vMx+J5BeEVSHhN996mywv11qNJ8zU7t5FbkiS0Sr4RI2pl658D4lUAB4MDJsXpJGh\nMnSa7kF0GwvaPbdA3H2Y8uZ7LpU3nhcoMWqevbLJ8y87Pf7tdw6JZCfe3W4vs4NMyOtvuMVqeHSf\noO2Ob5IFzW33FK9eWaOQlPYsWVSF5bq9eEnZqwn6CZ5SEARVZWNrzhlQViYrgFrGAfg+lHvF5voa\np2dOz35w/wFnA5fSfAzcLE+QgTdxM6wxVbRxA6v94F2MxHad2IJpuWj6mkAyS4IooN91m+/e5lqV\nEpyjV+4HVuQ56VwuOIwIxIAr4ozhyB0jHoIkUuJpCMqBaiEXalj7QRW/oX0PLT0YCXO8MmtT+wwl\nQ+3waMDBvttkhmcTEGlvZ2eTvesuxmD3yg4NiWmazXPiobuIbBKTZKtLtRbLkRRrKwpDQ2SSk+EY\nT9LJ/+Ev/zP+9k+4lOHP/cDniEfO0LvSa3BFDK5//uohn3vW3ftXf/LTjDMn5axf/xHyXOSkr3/A\njaddIbnB4TvsSiXq3/jDIRtNif1I4qqJ7fZmh7ORFGRstDmVRWN9c5u8WL3qsuf7rG84SVR7Hkay\nvC5c3sWXEgsqCHnqOUdzZ3lRlRfZuXCB7U0Xz+D7mtx312Y8DTL2v7NNYCnvnf/zd8Y/FUVZDkFX\nFcfzoqiyY93/W30BT86dr7C2kurCImdbdvcvbnX5UYnteaGVY++6OfctNebCrnsmh7MphyIxH2hL\nXhbHDJucDiWDLplRFKW8b/HKhsKeRZYzNgLLJSllsd6PmEzdGBsNh6zL9bSu75Ga1YzioshpSbbR\nzmaXXHpRJtmiMpquXb3M7q5svPGi6gG4t9Ukkc4BodGkEsuYKbi864zpSxtNnr/uMmgVOVnu5tD1\naxeIWu55HJ7NWEhGYWThxZdd8c+LFzbwRf6dTkY02pIh2PDJnmAuxicNAhl33VZIX2KFWn5clTHA\nQhbJA8/AE4NeB5ZEKpdPJobRbFl+ZS7lHGZ2VDm2xhi0mN9FbmmoCrVNAAAgAElEQVRJBXwvKzBi\nKAVBVFXkz4ypugT42iNb1hpBP0E23zxJSbOyOriqBu0snjOZu/U9twWejP04SaqK8grFVAq3RtmI\nv7LtjvPD/QD1guuOofNt/DJDVBl8qUDvYatsc6U8fCkf4/u6qqavIx8rPQF1Ab5cp/FM1UdvFZyc\nPSCS0iSKnDyT2MRQcU36YV5++iqXJExjeP9etdZ6FpRYvA9OTziTOLWdVo8/vO1iwfY2dthZcwzL\nqMixoXt3YdTgTKTtxXRIfCqSovKYi1OKVVUz9fXNTW4858bwM88+y8VLq0nuUMt8NWrUqFGjRo0a\nHwufKDO13d1g6juP4N7tezx31UkImTF0Imcx9tsB7YbzPr7w8gahWMuz2ZSdLWe1vnDjIr54ct/4\n+hGFeCUvf/9zrtUFTmZoRo4h+OZbH7IvTEMr0rxyw7UKuXqxXUlQiyRES7sPWzwkluBclMIUq1vg\ngR+i9TLr0NhzXnVlu5pKxbDWVmxaQ0c8c8N5E9PpgKevO6v4+et7+CIbFIYq6HGyiKu+dMPZopIX\nm0FIv+xgHvh0hNdttRo0pEip53mVV9JWhkZ7Y6X7y7KceO4YkIWOabSdV28LSzYvsyyWcozS4Emw\nZKA9rEg/uVIgQayFZ5jL87ZxSia/NQkVMzUaTitvae/SLv0NRwdvX9ymv96pnu9YpMbRyQwjEnGk\n/aqQ6iqwCrJzrVZCCUB//pmrHBw4KfitDw65L/XBev0N1tZFGssmPP+Co4ZVMeedt12WyOCX/ndS\nYWWf+9H3OLr9TXfv0RprwhZ+608OuSCS02KekqplBufmmpM7f+sPbvLhQZlZ1mcqPexGsylP0Lbu\nEYaoKAr2Lrm56J+rCaWUYnfvYvWdsr7S5StXKqapKAouXXbzCa3Jz9eeKZMvzgeXnyvgeZ6ZsnbJ\n0HIu81U9obR3Hrm2GFPOG0MoTEnfJLzUd17sj9/Y4gdl6K8XM7INyWj6wlMcHrr73T895FhaXx0k\nHWzbedKpbnLzrgt6vXnrXiVjZFlCKGtGYAuawhjurLW41nP30mnDQNaG7WaPbtdJ7mdpyNv7s5Xu\nz29rJjM33tdUUGW7To5Svv2WKz4aRiHrUrvu3t17bEuNnvV+j7PMMb1rPZ+1vvvO6XBChNTm8htk\nudSQ0gG+zKGmyfjs825tuntwxsnQsXmfe+Vpru65eRlgaEiCy2SasZDjNJVX1dtbBWPvEJWLdJyY\nqhCzCSxxKsHHM42KJcOu2aYpPeY8P6Inbaf66x5rItUeHU+IJAyi346q4Pi8KJYdUpShEAbNas1c\n3m0ymVU9QpvNFs0dt8dsXb/GQth9gyLTq2e6fXj0ECMhCQ3PJ5CJPFlMSCQTOk1mKCOMXr7cPwhD\nrKydfWa0OmWdrEalVGRJuqx7pQFJNjBFgpE9SRlTJYwURleZiSor0BJwH6UxWlhCT3Wx3urmQ7Pj\nY0WOPDt+yB25gd2NddbW3ATsXL5Ao7zONGUoz/xP3nmXDWk1lQUFHw6cbD1VAXckS1TPF3zmgpMm\n1/I5t05EmkxTWDgWbHz7jOvbe9UjLNWnrd4GV/ecDPrMc8+xJxmsnV4X/QT3+IkaU0lqiWXQWFVU\nPb1OzibcEg318m6fPHcTcjcoePGau/nCGDxZzBfzhFjiWJIUclmFzwYpLaF4CwXrQqk/d22D0xOn\nJbfCNuulAZCG1cRI54Zs6ij73Y0+rcoY0FWB0FVwPg1cK7WMozDLLcJahbXn4qpKqhVNW+j5L37f\n5/jcp53EMh2NKkPJKhc/BDAajzk9ddlEg+HINREFAs+vMhzRirDsFej7hCKJBkFIs6wiHxXoxvpK\n91dgyUWGyJKMJC/lygAlE8HfaFTSa9Dwq16FkdLocj2YQSqySNHKSJVk42QZZaGMLEkr42tjZ6sq\nn9BZa9NslUUAfVS5GM5SvKmkD6dFRdl3ojbmCRa3JlWvYpSxtGUBWe96fOsN97ybrYhrF90z6/ea\nvCN9zl569hqvvPIcAF94us/BbbfpvD9rcfeW27xG977NvhiJjcYGH7zvMi8VcCSxXe1Q0+2VjWgj\nHhy77//q12/zytNuTphM88GHzqC7tLvGwcnqcWHWmEdKE5SG0ndm1ZVj2fO8R4yf89l5Zeq/MeYj\nSxec/7vi0bipauwr9Uihw/L75Zz/zr+vAs+qygD3MQQSZ3LN03xKJP09z1QdEVQQ0ZRK53vRC1jp\n5+hHHn3JAF6bK44Xztg5XsS8dM29361+j8N9F/eULmYcS6aczjL2+u49Pr3ZZjuQzgTZkLWySrAX\n0ZAGr2/eecB4utqyfDZaMJlLr7rkAZtlYcxWF6vdBjIaTsilBErkBfjlnGiGBFtuHdSqSScQSfNY\ncybFJzER87lkIjbbVe+/xSym5bmJ3PYWRNvSEHqvQxa7+WFiSxhKL8dOk6nIVcl4QucJij1Ojw1a\nevwZUyw7ZRjwyu0rNySy4ZskZiiyY5LAoRS7XSxS2i13/X4Y4YusE2gN5RDTy56vKFPJyzps0pHM\n8L41ZLYsD2AZz53ENpuMq8LD3+lEPw6nZ4cgEqoqLJ6sr8oWeOVxsqTKms3yZQFrGzQquS1JpqS2\nlPAUVvqUGi+nKAuxakUu8Y7oJkEghq0tXBwGLiNSQmFptPvYsoOFxzK21hQou3r/wX6/SSFjaae3\nTlfKAWW+V5UfKJRmLvduomV2uW61KGSN2bi0V+0J2Txmo+/mayMLmJy4sTecD/DL/afZ4HToiBTt\nh8ykEXer2+N6zzmKl3b3ePaqq+6/ub1No+0McM8LqxCZVVDLfDVq1KhRo0aNGh8DnygzdfbgDiPJ\nUEvGc+44MorJLKHfktog8YyJWLx+Pq2i7NF+xews0hgjbMSNCy2QgNnxaMhC3MzC2KoXULcZ8NIz\nLhPJ1wHz6ULOFVctHU4HM+YSZBgGG2hb9vJbdndfBUpTWfVKe8su5GpZdwdDVYBU60dr6lhhoLTy\nqkDgbm+dQmQD5Xm0pMDe+mbG3iUJpIznLEr5bRFX7Sk8rYjOSXtlkKELlC8ZMT4y8+qjYC0EUpPK\nDyCXbJbUpgQi5+mmjydFKb0oxEiQf1LklRelCk0kFHzejFFBWVssqDJhgkhVnlMYBfhSRwulmck7\nnMc5KinbDKkqML3IbBXgrgof/QRDvQm0xSNsKs3lDefRHp+eEkig9kvPX2QmHvw33rrNDakN5FmF\nlqwqk3j80Jdccbqf+bF/n+P7jrn4x//kf+Vk6o5//akuD04k0DXwODxy82NzPeDpy86zHE5Tjh46\nj+qlp6/y3NOOEXvnzimjifPCtTJMZ6tn83me9wgr9FE98r7zvz+q3pO1tvr7eRbpPIP0SG2p75LN\nB0s57zvZp/P//SQ1pzSqkrJ9z6Mj3vmG9tBSXHI6nDCtAm9zOp4bk1G0we6mK+y51d7g9NTJeeHx\nAWnq3u8gLgikRtH1rT5h4iS3ZrTO/dBJEYvBMVfWxSNvFqz5btyqIMJrO6nRa66RShD/Ir3HIlut\n59lirpiM3XxqbHTJpQDx8GxEU+boVq9DIgVu0YautM3Y3ugxGkiCRpoS+e44zz19mTv77l6xeZXZ\nlyYpoWQ5bW72q4Bgirhi0xvhkiHKk5y5nFcF+pFA5/FodQY1vt+v6nRleVrp00GgiYS9CAJ/yS7l\nhWObgKIBc+2uZzSboyQLrxlZPAklyX2/umabF1WhzsIWVXjEetQglgKPrahVsZ3TomA0dcecT2ck\nIhtZRdWnbxUkxvWjBLd3aOk9V8xmUJTruHUZskBKTiCJPPZcMWuiiEKSZYzJKYNTVA5GakulWY6u\nkjh0tb96nodXJp54Hg2/VFeaFCJ1JYHG99xaZbIU1OrMVDydQCaMm2/oCItaWL9iixanJ2jZKygK\ntOzlV6/uVvO+iBcYYR4Xowm7Mv/SZMod2f/8Roee3EuapySSDRXokIWsJc9fu86urNlbGxuVzO0H\n/rkehYYnyHf5ZI2p/Q/ewkqFcm08DqRHnvI0XSlEGMc5s7mj65KzZXaF9vyqR1OSZdWCU2iFlQE0\nGoa0pQ9VqxHSkKqyubW89KxbrLCKLM2rzyWN5yldnSv0Wyhbntd7ogJsKF1lHJ2Ph1L63KBXPBIH\nUu4PSil3PhylXUl1vo+WTMDCGFcQEQj9EE/ijoIwotdxm29eFMtzYSlvwBhTpQErtZSyrNWYFTOI\n8swSeuVEg1ykk8JmRDKcAjK0NMJKkrlLx8DJnkpiWLxGgOpIOnOoiMpmo75iedu6Mij1+QyvJMXk\npVyi2JT+TFvrF6qF7vatDxlIX6tZnFdS4CrYRFfvJFCwvu4MKKsKNqVS/MOjGdtr7u/PXt2tmm0H\noU9L4tt+9ffe5O4DF0/yQ61f5e23Xabm4cMRnaZbTJ668Tx/+Bv/FwCfutHkvrygwAvZf+DmR3ej\nT4Yzsoo8JVAS79FoVxscKnCr5oo4b+A8YqA8gYzmvm6fuBHxd8/o++iJ9uc1pjLto0W2sbkhlTl9\nWIT88dgdMzVzfJEQrrUVWprwmuGCmfRh7PR7tKX/2V7TouTdRa2CTLsxMI4TJlrkNG3Zk9oLcTTj\nQtOdd68bcUFi/cKoj/Ekvqi9w8OFxP9kllXTh9uNECXdH7Y315YSW5YSydoXKcPOjotJybOU2dwZ\nfHduDbkgjV6bQYeF9PXzGzmXLjrHs9Xw8GVLHs8WVXymYVnVvVAepqo+kNLsSIZw6NMQeTwMAwqZ\nH4PxlMP91ZvHdttLQ8zzW0Ry3sDTRGJQKKWqgsh5Yapwh1BrNvtu3R/OFlX8S6vRJozcJtyMWiSy\nH/hBo5r38/kUX+KzcmUZJuKAK1VNEb8RVutpoRResAzv8NXqW+vUarTnxpGvcwoZCwWm9A1Rua2K\nglqlKk3JJyOXUgqFBk/WWuVpVCmZFYqyaYYKfbRZOk5ljGOBQsleor2QXPZai1dlp3vaR7GUXLNi\n9ZJBurDVnvfeu+9y55Z0vwhaPJQinO12m/lC+otaF4rizrUM8fGUBokFu3r1ChcvOqluP88YjNzY\nJtYcnrjwB6U1HSkQ2gjaPH3DZertXLhAW2L3PM8jFaM1Mzm+EBfNZpM8fxLnrUaNGjVq1KhRo8af\nG58oM3X3dMFWWePCN2RihUaeZTqXUvapjy/ZL0GRoyVbKddq6bd6PlMJXs6wldR0eDrHFys6Cj0i\nCUb3tS4ZRqx2zBYARlVZgaagKjKXjGd4QnO2Wk2uXLqw8j1alkG71hi0HMdYSyByQp5nVFyiNeeC\nbc8zREuPX+tlkU9tbPVba03Vs0jrCCMSWmAKKjbKFkup0RqMKX/rqGxwHdWLFTOmPOtRlASyyQnL\n3lcBdCN3nk6jIDfO071zsI/uOO8wbLYIpZ6NH4H1ZnLfQdXGwVc+/jkPSYms1ggbFZvTaTYJxcvU\n1qfdcB5+FLRIJfvFaJ/sfacjzwYzYqnHtBK0RyDeZ4eQwakEbNLnUNp0tBpdPvXDXwEgmQ353379\nD9x1Njq8+obzuuLFmIX0o5pMYr79UFjZ5jrf872fB+Dt179JR7prnAwMH9xx7IJPQlHKpmnB8dD9\nttMJOZFWLnE2pyn1e+I058+Z9IaT2OTTisxPOWb/PKxUeYbzEqHDUuZ7kkDz7wbP2mo9UL5PJuvE\noMgZSwZUMs6IhUl+tudzxXdrQ3A2I5IaRS9d2GBzw2UKtbvrBCLhGc64vy9ZwlnGCztuHA6O97GJ\nk8pu7LW4vufG7cWdDhuS9VmoFon0tYxabXzxjH1fEazo4yqb4QnPl8VnxNKuQ2mfqOwxFwWsSwB8\nkhqUsF7JYkGycOtstxMxLcMdpikdCYa/drFHQ4Lwi31TyVja01Uike/79LplX0+PUKT4MHCFIAGw\nFl/CCAKl6HdW6wMKoLVf1XIityQShF14ukpCKowhK4twak0q1xl4ikiup9duEUsgfuBHtISVaHQv\nsTCO3fDWr9OW7wdnN8kmb7nTqhFGChWr2YyFjPndizsMpPBtnuc0RJZqRBFpsjpLbKzGZkvGp2xU\na7W37FmrNZ5omQofyTUnsMvC0MoqdJnkpL1lkdLQEqhliETZOkgrCCs50qM8cYGtfut5oH1RV6wl\nz0Si9XVVP3AV7GxsVzXOxnbGfFHW/xrTlHdhCkNetuo5t65sbm7SlppTi8WCsSTvnA6HBBLCMp5N\nq3CcPDdEUmdqc3ubTcm49JRXZbMXRVFJq2enZ+RCMfb7fQaDoZx3tQz3Ep+oMTULNskkvTNfJBiR\n57JFBvJ5YT0aEt2fB4bJyG1k6bnyBFZlSz3YqiqWajabVVJQdq5B6oWdLbKijLdKK2nPWkVQVSr2\nyEW7Hc0HlaToac3ZdHVtOEsLvLInmaerhUChKMrBZ6EcuBZXRK76flEaPrYygs7vksYUVV8pmxfn\nYq+8qkedcjqi/ODR0gymahbt6Hr3PJfG1+PQ6bWxYhAn8by6xna3QyAL+CzLmUghuXkeE8Zloc4Q\nX+IczqfIa6ieWeD5VQG1MAqJJGaj1+my1XOZG5udHZrSUzE3RTlH3bUE7h3uXLnMwQPJnjsYE8fl\n8vN4tP2IrmT+bLQ28WSja+c9tj7lDOtJnvKrv/Ov5BoMGzLxBqMp1yVG5mw44lBsuG+8+5CRfH7x\nlU+zf9tttmf3XuftD9znh8dThlIE8tp2C1GNuHvvLqUr4eUF752IgTaL8SSWg9Q+UWkEUxTV2NTK\n8uc3XR7/S3cae+7blb68/LUFy1KaPm9LVf0tn9DIuhCfoSV9XmsoAyByk1d9Cc/mBW/NpH/iEA4C\n95LW4gXbMs86g4LNTKSUpMOZpHgfTY85lZgZbXPaUv3bU4qW9Mnb3N0mFGk4brQ5086xSHKPRGSS\nKIVYxnzU6+NPVpOkQ79NUbjzd1sB3dCNwcOzIalkhxl8CpF/g7CgXzYO39tAyT1ZZq7gKvDBgwmX\nNtw8fmYvQ0nZmUuXtlgspEyCdY2GAVqBR7MpTmJsq8KVYaQojWOFruIzN9d6leyyCrb2LlTZwLYw\nUJQSFVVcqMkNE3Fa5ot5NVdyY6v1zvOWYRbGpChcvNq0+TkWDZd9m4UtstL+622CZDi2vLfwH7h4\nxzjOK+fdFLZamluRz5oU1i2sXYaSrACtiyq721hdZdUZm7t7xsXW5mW4hIezhHCKV2n4YO2y56Cn\nzvW7LfBKhx2DkbGmPb8KQzGmqJ6b1WGVme2pHCUFPy0htmojsEA9Qcaij8dUGggro0ilYGacpJiy\nz26eLYkIu8wYDoKA01PntMzni2q9nw3POBq4vysgLB3voM3ly9cBV8ZlY9sZU1o5QxcgiqLKmIqi\nCC3hEu12m60t5zj5vv9EzmIt89WoUaNGjRo1anwMqL8IOr1GjRo1atSoUeMvK2pmqkaNGjVq1KhR\n42OgNqZq1KhRo0aNGjU+BmpjqkaNGjVq1KhR42OgNqZq1KhRo0aNGjU+BmpjqkaNGjVq1KhR42Og\nNqZq1KhRo0aNGjU+BmpjqkaNGjVq1KhR42OgNqZq1KhRo0aNGjU+BmpjqkaNGjVq1KhR42OgNqZq\n1KhRo0aNGjU+BmpjqkaNGjVq1KhR42OgNqZq1KhRo0aNGjU+BmpjqkaNGjVq1KhR42OgNqZq1KhR\no0aNGjU+BmpjqkaNGjVq1KhR42PA/yRP1vAD+93+33f9H08E9RdyFIfzV+SOm+TpY0/wT//Rf2V/\n6X/5pwDM44zZvHBHKzIubjjb9d3bM8apASAvEvbWIgA8HXIyzQA4HozwrLuGl195hfFsCsDB/Ydc\nvbgHwMWdLT64exeA2/cfkKXuXAbQSlW3YcuP1mJteV/LW3F/c3/P8/zPvMev/J1PW2s9AOJFzsnx\nwB0jN5C7Y2RZSq/bd88sLRgNJwBMxzFGTq+Uosjz8hLxIzcUlaeqawuCiMJ9BZMbbOaejbIWW7jn\nR2Ewhdy3tQRhuLwnuRNTGLI8ledtHvsO/+v/9u/bv/+3/3MALnS3+ZX/+X8AYHD4IZ/67PMA7D11\ngcs76wCkkxFHgyEA25efJT52n/f3jzg8WQBwnM3Y14cAeF7KtUsbAHz583+XtfazAPz+v/gVXvtX\nvwvA5ec3Cbvumew/mPBXf/Kz7u/rX2Z4NALg//3t3yVX7gFd+swE5S/kmP/9Y+/x3/4Pn6se0Gw+\nZOp+SnOthVZuPC6UxZgEAN8PiFP3DKfjGc3QjeXQb9Ptb7nvRAmLRQyAUpDLy/YDH5O6d7eYz2l0\n3H3lhSXw5X0VAYVx7zHOYybTufu8MOTukLS7IWHkxt6rv/zwsff4//z0f2pD7c4Vag+byPjpNFDr\nLXdeW1AU8ndtSY37PPAKdq5cAiBNcsYjN/+K3BJtut8ejo84OTkB4Bvvf8jv3rwNwNxvEMj1p9MZ\nl69cAeDnf/7n+Zl/5++7Zz445K1v/qE75mLIdDoG4PKNp0kyN7a//Nd/6s+8x//i/7hkjTzjosiJ\nTABAFmfoyH22oaYw7r35ShH57tobQUiRugebTQzf/D33nv/4tydkc/cMmr6i414JvUAT9tyzf+Xz\nN2hcdM+195kMf91dZtO7gm96AAyTEYk8g/l7Hq0X3W+9lk85Bf/Lr/7fj32HP/VjL9pu5MaI9iIm\nsbugw7MBiYwL3w8J3ZBF+4pmw32/5Wnywp3iZJxyOHA/SDOD57nx22g0kY9c2VsnkHUzixM21tYA\nuHf/gLOxe/8ag7JuzjXCAL/hnnOe53Sa7tl2my0Wczeh/s+v/8lj7/FbHzy0haxhvu/jyVocBj5h\nWN67hzJuXChrUHKdvu/je+5daK1Qyt2MUpDl7pjfeOMNZosZAN//hc8hU5c8M8ghH4W1aDmO53lo\nvfyMcj/QSuEH7t5boX7sPS5O59WG6nk+Ssm+SFEdXymFNvLZaqoX43m4XQ1Qy3UdS/U59zRKzuCd\n27oNBiM3aezyZj1jl4c5ty9aq7EyPosir37b3u4+9h5rZqpGjRo1atSoUeNj4BNlpnrr239RFBQf\neSAF6gnZKVse5y/oulqdJq2281Bm8Wh5YAtWLONCg+c7T60bNOmId6mtoS3e0CLPmYg3FIQBN7av\nArDp+4ynjpl46/U/QSvnHez1NyjEaxjHMdO58wpNsbwxhfNYwLE45WelFKWH+zgMjk6xQnVp5bO7\n6RgoCsvoxF1XEcN04K59sVhUzJG2OSU15fseYeCOkxdF5e1pPJRyzyaP5ySJ/F15hOLBWFtQeira\nU3hybQaFtc6r1lpVHk+BrTy2VdDfauGHbmoUJqXbcd7h3s51XvzsSwC0dtewY8dKTB4c8PD2MQAP\n705pa/f9dttns+3eTzz0aAqDk3lTGtqNkcnxmMnDD9yzPZkzTRoAvPbqkNLX+b6/cgU/dMdR1q/e\neVEYPPHIN3pPEUSdle/R1+CXLF4RYYVVDHJLIM8tswVK3lFhFXHiGA5DisFdZ5LGtMQD9oKAXNiO\nwqYkwjQ1aRHP3Hj0PYs15bONEUKSNDfMZu63RsVEnrvHWbEgTuWaM4sXrj6/31+39IQ5iKzGynFu\n7OwQCkt1eniIEjYqUNCU+dpG0YiPAFB49BPxpK2qWDZ/5tM/czeQpR3eF+f7rprhydKqgybDU8dU\nfu0Pvsa/9qM/DsD07D7HDx2rHJCSpo7JOLh3izj7KLrgT6Md+hi53jTT2FzYn6bG891z8kKFse4Z\naKVQwl4f3E5575uOrdh/P+POh+79KFOw2XXHCY1FJzLnGpbUd+d69/Yp1z3HrHZHlrDvjpnNj8B3\nDJctRmQL932TdUG3AShsgeeFK90fgNbgBTJe8Jml7vhZUdDtOWa411sjjNy55vEEjbuewIdMWEfI\nqnkcNVrV8fO8oMjcwNDW0u923XEsJLKGUuQ05BryLKHVEXavFZHJ809mKbPhGQDD6eQRduRxsNae\nY08svjAyvu9Xa5g1hnIv+c4ZUDK6Bk1Jz2gFUbOk6/Q5RcJWa31hioqFOQ917iRKqXPMEZTLqPaU\nI4xWhFIWU7E/eXUvqOXWqzinomCp2ChrKeS+jLZ45cVZW7FRNsur45i8wJMLtf5y3deoar+38nv3\nr3PMlDHV/vaokvN4fKLG1Itf+uvVDXw3qFWvXX3EgmOrf6yMR759XgI7N8aexEALo5BW2y0c5mhY\n0bFWWTzfPW7P89hsNAEopjMWMzfhs3TKhb77/na/S6vjDKt5vGBnw03yvUbE7XedXKR9D00pUejq\n+Jd3d9g/dfLb2WBQXb1SEMh3HP1ZDiY+mu79CCRnBZ2O20i7vSZbG86Y0loRGGf4LKYzZjO3OeRZ\nhpZFeHMjwpcZqLVaLiDGAy2yhPZZzN2mOomTc5S3JojcxDCFxeQl5S2UMG4iWhlASuslPW0UxZ+t\nXj6Ci1sRQeS+H88T1vpu8Xzhs8/Tu+ykH2MXmMTJl3meMJ67RT5dHHNn6O5d4xYRgPGioHHi3vnl\na9tstt27vfnqq5wcOsPzwdEpZydO7jEovvcHnOH23Mt7KJHeUFQTPMtTOl23qT1z9W8TNVY3piLl\nUcg6ExAQ4DaU0PNJRBKwfohRpWFF9Qw9z0d78tnXJLHbdKKwjTbu/U7nGVY2dEOB0u7vfmQpxMAP\nAg1KDJlIoRciH0/hwpYbV4s0x+SZnNfQKjeIFTDZbBJ23LzJC0ftA0z2urTEWL6d7WPEsGoGPmG5\nkSlNIDuiUlCIodJud/FikYJOh7RbTtba2szY7Lt3uj8a4gVu4/bUcon98MPbvPbatwBIJ/tMTp0x\nHpLRbLrxf3iwz3SRsQpMnhJGbkxhPdLcjcEwsJWkUSxCRkO32Z7uG2697sbmw7djZmei4WmDkjna\nCD1EGaWpwZfDF75iEch1qQVa5Pf8JCftuGMaHROuuWcTeikL2eSL2IJuVOfy/GCl+wNothrOogLG\n0wXzzJ14fXOH5555GXDGVEcMpXky5uD+LQCy6Zj5QuQ5rYbd1AAAACAASURBVPG0eyaBV+DLOph5\nltaaG2utTpNM1jDla+KZmwebW2tsyPUMJwPWZM3zIg+v5R5Qd77g4MEDdz3dHjv99ZXv8fwepJVy\ncpogk9AGC5XTaLHVvgJOAqwgf07TlNMH9wGYTCbV/3Zyoqy7ha0MB2dAiZOsdXUNTuYrP1MZ6c5Z\nXfkW3XpRkgYalCiDSunqXrTWmNJoolgaMhZyKwZ7nhOIs60LSyDXppKUdCFj++49MnH81i/s0uq4\nddFaS9R07ytstJbMwiMXqisbTmv9yHN+7D2u/M0aNWrUqFGjRo0afwqfKDOlu9uPZ6ZWOtIyYPoR\nu96ek+1WOseS9jsfzPanGK4nsE5932Njw3moH3xwvzrOMqzaBRDmU+ctbKUxmZjCsVL0JNhyETRo\nRI7hOjs75FLXeeQdDzxhBfYCy2f67juhVhxJ8N770yFK6PDA8yp50dOaQDyO3IIRL+xJAvf/k5/7\nz6rgRE1BoyGsEAUnp47mPj44YXh2CsBsOqDVdOfc2exVbNFkPmMq0s/J6SmHIhHOM8VMWINGFFGI\nZ9ZsBKjAHcdYSy6eR5qmaLl+3wtQFfOlmUkQaJabyhNaBc9f7tII3HWmRcbmuniu61tL2jgzWHkO\n09gwkADuzc2Q7XX3rv7g1ffZP3DPJIwiJrF7D/2bd/ieay6JoNlSmMR5z2Y24akLjum49NQNnv2M\n87y31q/Q7gkDmEVYYWULaxhJsPDZIEacZDY2V/CKkwLiMtBf0ZLA7kylpMJ2xvOCwhOP0Pp4QlmY\ngspjM1aRpaW0mtAU5ihJAiYSjN5qNsmF8UmKGZILwFa/RSBEk818soY7Zhr7BCLPbW02aMuNZamm\nLXNiFaz1+oQNmTfNNh1hgw9PDjGFu4i5V5D7Igt1mnTX3dydLGYVm5bnBkr2s+9jRM77wJ9hhO24\nxZSBsEu+6dFrOUbsxlPX2d7ecc+h3eLuQ8depON9tDB6/aamkESVeV4wWZGZevWfx7TakVyjTyy/\nC/yCydjd39nxjNMDd42jswQhHYki6HSEuQ00Rp5BI7RYCdq2DfACef9xSFsC0L/0hTWe6Tiu5mA6\nYjxw3999/iJKmLLQFgShG/tJeoa1jh3QOqewi5XuD6DTblKIBK1CTUPk5bX+FrfvHABw/XqLD2/d\nBmB7t8fa2iYA09zQ6bhrXhQxVrvx6OmCXtddTxj2WNtw8yWMGhzuu2OmKieR0ANfaXztrmFnp8va\ntpuLOTmJrMX9Todm95q75maLSD+BBnZuTyyKgkLWqvzcHqS05qOooKIoKqn3EW5Ewzvv3QTgvfc/\n5Etf/H53zCzHnmOmqJhTi6eWa+eSmTrH8HsKrSVkQ6tlktNKMJSUj9tqRTrEBe+DW9fTQlhOk2Bj\nN56TyZQyQJ8A1jtujVS5ZR4n8v2cUALxx/ducXzg1Jvh/jZaWMizs1OefeFFAJ75/PdTLHdksEtJ\n8c+byPaJGlOWgvNGykdd8soqn/3TupRdUearxDylHjXuzodPPWL0PQHVpxSbEkek1fI4iuXgK2xB\nQwbuZzf7eEKH/9bBoMo663W7PDx1EzvQHle33OJ1duc+V4Qm/3QE3yOGSnt9g0HgFprsaMSdyT4A\noa8rOU8pVU0kbZfyZWFZ2WD8N/7eT6MKocjncybjY7m/nFziE4pkxuDYUcz79z4gj90K7tuUXCQb\n6/UpRffJbM7BqZO3jkYJ48RdyyK1WPl+Fk+JJa5kPB4xk7iIUAdL+d0qjMTvaJ/quSZZgn4CaWFt\nvYevJS6mk3PthYvuc+RjjZP8NHPmImXu3x9hY7cg3N+fc3nTnavTDPjwQLIdUexuu3d4Z/+Ed9+/\nDbi4lws77r2Nhgu+/Nd+GIDrz73AhcvOmFrfeAZPrA4bzOi50Cu+8AM/yP6RWzTe/tbX8GRO/Mjf\n+NnH3mMSFliJRwuNQdhvAm0rSWthICvKrNOikmtNsZRNTZYS+u7atClY60psTBqixfAMmpClEjNl\nfLJMNm4voinOg9UhSejGibfWxMi7azRCZjO3YK6v9VFPsGR1ms0qTsMLfCLZQKN0xnjkJLY0UCQi\nN/e6Eb0ru+7v9x8ym7jrCaMGjYbIhdYyTNzf7yZDbu67uKfT6YJAJL8f//xn+P4f/qJ8v+D117/t\n7j2KiCQE4OrFl7j/npP8chLaHff3ZL5AFNfH4tf+0Rhw88ZajS/Pu9U21eaQFBrkvUUBbGy45+cH\noDz3/r0IoqZ7n1FkqzWiUBoJFSPWhl3lnsH1zescHztn6c23Z7Rn7u8blxW5yECq6BBIsJvNh9Vb\nU7mHF6w+F3UjohG4wWnbis9cd9m0rajPP/nlfwbAdGZIxCF58+232Vpz13N5d4NAjO9OW4PcbxD6\nNFoyn4DhxD1DpjMQ46jTaRLJvWz2OjQkBS6hwGuX2dcRWVYaAtCR83paPZHmY118gnx2sZAAHgot\nTohWS6NG8ajxRWmMFIZCxvLJ6RmxrIVHgykfvPMWAPu3PuBLP+jWmGYzJCv3J1X5C+AtwxPOy3lK\nWbxSklNPFp1ssedieEwl4RUGHt53e0WapFgxtNsRhImQDAentCRmVEce+ZFbU2fDKW+9+QYA65e2\nufHM0wBsB5pI1qE4SYjHjrgY3b3HUDIu5zeeI1zfWN77+bCecxnnTxIzVct8NWrUqFGjRo0aHwOf\nKDOl+AtM5vuo459PQ/g4x+Gjqkyt/ut1CWgMo5AkXVR/L6WRQPlMCudt//405oWm8853Lm7T6zvv\npggCWlIrJk9TOpL9d6/I+PQrri7RZ7a6aMlEeudgwFuSNfTeYIItA/NsVp3X/Vs8IFhmV5jVqU1P\nRxTCMty79zZ//Ef/0l1jNqVRZloVBU2R6pLZHFXKifliWdPH8/AlQ8YkMT0J+L72yos88/IX3HNq\nb7JI3TUuFguSufM+R8MhxyeOEUvipJJq8yQjrbJ9cg6P3XdOBwNG4/FK9wfQbF1H+xIw2xniS/ZO\nQYRW2+76zRHTI3f8C4GiI9Le7bTD2cidK56MWYhrP1rkYJw3r7XCyNS7fTJjLJ5lhKWQzCujQpTv\nmJQ0S7Cxk0Fnk2MGp451TGcDotSxJLP5YSV9rgKtM6bNkkUAtPtt4CuaLTd2WtZD4j6xM00smXrW\nKhpy/bldesxZbGl0nTe50V7WhIKMUNiIvPBYb0iWn1JkQtM3Ap8tqas1TX3GUqNIAe1QmBUdMy+L\nC60AhapkbZPlLCZu/HiFJZDx3u10Ki+8KCAdu/naGM7Iz9x7DNoeQ+s+f/PD2wyFcRnO5nxw3825\n7voG/+A/+I8A+Mpf+wpff+2PAfjHv/SPeOutP6mu4eWXPg3Az/38f0x3x9Wfiof7dPtOmlL+DJiu\ndH95Zs6x6cvP3ZZPIKxKbOHFz14H4Jlnd3nn3fcBeHj/BE9kEYKcRlPqhkUKTyTuLLd4wkAuCkMq\nLPFgEDNJ3Zy4fuN5+ltCax6cYBsi+YYhZxNhlRsRYdfJp3GRoFQZXvB4hBublMtHrx3x1a/+BOCy\nF3/jt/4AgJvvPqhCH7AegxM3z9JU8dR1N1/9oKAp77zZaeLL2JzGGYGMTc8oIslw7TdDtMi2rVaE\nkWSE3GhiWTcXmanWJ6V0JSflNsc8QViBwi6z5LSu1IzCmEox8DzOyXznVBmrKgnM8xTDoRuPv/c7\nv0lL5LBrl3d57/WvAzAazSlS90C//CM/TNDryjU8ckFLKdAW1fVovCpLTnGOtVnhVlMvQBVuDQtM\njpaQgXQyQo2cAtPOLCUtq+YFRmS+aDolkHNNpiMGElbQXF9HSHHiw0M+HDlZ2RYF9/bd57fHkMsF\nqmzBcP42AO+cDPjev+oYumdfeJ5cJN3ABqjifLzP/0+z+f4ywBpDV+SEdrvBeOw2Ba2oNh2jFalM\nkruJQrVFbol8JjJo1oOAkzNHZwZK4csgfvfhEe823UZ/E0tD5J+3bh9U2vnRJCaXapeedZs3uAyx\ncmzkRXEug291ytbzfJLcLfY333uNh/ccfdxqKKZi2KVJSjN0i62n/OrY2mZVCngUBfR67jltrLU5\nGzqj4PTgDj2JD3rl8xfpXndxCLn1MWWRT1NUWS5ZnleaO0UhKcSwSBPGEpc2HI8YSVHNVdBuvYDV\nJQUcV0UPtbpULTKkQ9qS1aOCCQdlTbqwQyCGr/EiWr7EH2nFJJY06mxB+SI2+135b7i4s4aW0g6D\nw0NmM1fU0Q+B3H1nMRsRS9ZKniSIGkZRJJVxvApCHdEQS2nhLUiUM2p0rknL7B0VElXZfJZIuY0m\nCDVrUv6j0FOQIolh6BFI2ruOFviS8lVoWODGaVYYOh2Rl5XFShmJwNf4hcRsdDr4Mmbn0ykXtp2h\nYazhrkjGqyDJ0kr2DbRHPHW/nY+HaDGg2mGTtsjjZpFydtst7N5gWNqXHA+P+drNOwC8czakkNT6\nsNmit+Yk4J/413+Sv/5v/k0A/ugPX+UXf9EVer353uvYws0Xmxvuvu+MmZOzEdeeczLu/XczYjHQ\nNBpVxoc8BkqpR955KYGGnke36579Rkfxd/6ekxxvPPMUf/Sqew/feO1txDbi4PiUQtYLpX0K2UxU\nYJCaquhYMZEYxPFwn52d5wC4cOVldnZchutwfML9sTMizVrCaOoMX7+nUFIs1OgJmNXn4jS3xFP3\nbC7ubrF9wZ3rjW+/R3/NzVHfP+ZM1sp+vwcyBu/tH7Kx5c67sdamKRnDcZYuC5n6qirREmoPvzQc\ntCWVeTk5PiMqHaogYCaTbpymzMQZ0FrTTJ1RaawlDFaPmfIDv1q3UKqS8R4NkzKY8h09kmkOWozi\noOEzOHXlPPLZKZOpc/bCVh9P1uw3Xv8WZwP3rF56+Wme2XbGfZZmVWyti04pHYxzY/FJaiF8B1Su\nWBy7eMHx6QFGwllO791Czd1nb54yk0zu5uYGDcmIPXjrJnOJxx1OhgRSvuKzP/YjXP+8m0Pjacwb\n77sYsXg+5zRz++43TJOphLl0fI/rQlaER8ekf/A1AK5cf6oKSbDGVGV0jLErxWCXqGW+GjVq1KhR\no0aNj4G/NMxUGZinlXqi2hFPDOOy0AD6vTb7Qjeel/nAVNlNXq4IxQJ/7/4+ffGqvveZG0yliOHF\nrU1y8YZOZynDY0eX3r1/TFgWl8wT+pLxZ7AVS+Upr/JujIFUAiYLYx+XWPmR8HzLYOA8jNOjOwRS\nJ2g+Trl338lPuTH0eo5i7nX7y7pI6YJSN2o3A4bSQmNjvcf2nvPwzWjKg7uO7ZrPZ7z0WUfFXnv2\n05jQBRVaC81SomRZ8M7mhlyYiHae01t317aVpmTZE0hg/vXKI7NmWWgUvcAihUmzAe1tl5F3Vuxz\n76HzAvVuh6As1KMjIpFMKOY0Ws4zDqMmHzxwHuR6t83GuvOWFvGUg2NJOuhuoEfOg/Q81+oEHpWg\nc2MohF5QBFV9oFUQBgFZmQxQFFhZChKrUOKlRS0fr2zZ0YCFLbP/dFWzqdUKUb7IlM1GlX25yBM8\nueaGvywiaQOfkhJZFB651JyaFQuawlL5GiQunVxT1aLSXkAzWL3gY6PZZDJ07+vs7IxW5BiosNUk\nFaZvNBlz/ZKT25JixFCkvbkPEofPByennATCeDY0njyfPM/JZZxEUcQ3pIbUL/zif8fB4YFcQ4tU\nAv09XzGSmj+/8iu/wr/7s/8WABf3LnJ01zFWpsjpr61Yo0gr1kSm8TwYj6Q+GIr1Tfecbry8RX/L\nPfvB4pBL1132QqP9LLnU17p1y+PgoVunlFnWEzM2JBKJdbcFszP3OY0T0rmr5fTB2zHF4ocA2Lvy\nAqP5PQBOFm9iZm7ONXdC8N3aYE5GfP233VzhRx9/i2fjmExayDTGC04HjnF449vvVC1bPM+rrjmO\nMzalRlmaWI4l9GH3wtM0WjJzJsuCuOvdLru7FwC4sLvrtF5AF4b9e+5ePnj/JkrWlcwYSpFSG0so\nHdK0ZymEmR+P5/8fe2/WZNl1XomtvfeZ73xzrKysEVWFKkzEJJACKA4SuzXQsmV3W3aEHY6wH/zm\n/i8O2+FwPznC0aF2y1ar3e22LWogKZEgiBlEAah5yMo57807n3lvP3zf2ZlFkaxbZgTcD/m9IJF1\n89yzz9njt761Fjxn/n7qugqVipSU0mZtlDhKBhmUx7JER2uJ4zhwK9sVATx8QALAWw/vIJ9RX2t2\nFzAa0DNXtRqef/VVAMDFixfhMzFHlNra/CghLVlKHtPrk08BXf58ONMEg09pXt//7EMc7HHR+WwA\njxmFoiiRVUzf0+cRMsIz3d9BHlMGcDKdoKIvjEcjtJYpO2kWl1CklD2cbDyCN2UtuFxjyiK4S6eW\n8e1/9EcAADfO8cUHH9L99GOsMmM7NRoZl6IYrS3yM1cbn+J5fInxi1/aY2u/EL/ot784jMHaEk06\n02mCcczDQR7jRZifU0aofhaAeYrNl9ZHvk+ddgsQG3wLR4u+gIHDWd0CuYU6pHRQCZbHkxEMw1fN\nyIfOWVlYG7tA+9KFV4HtjmclCwQ0RMXaE8qCeHmeHwl1/nxl2Jwbq8m0jx/88C8BAJubD4GEJrRH\nG3vgbDzSNMXhmAUJg7F9VfFsikWmntfba8hymvx3egOUvNBJJTFi7Lt30LP+bgtLy+isXqJnpsUR\nMHmMMQktUVRK3qWGxxtQvyyfQg0WAGqAoM1OmQ6gquHrDSAMLc7Sb2A2ovfzoD9GP2aRuCK2G9/D\n/tiqfQsBtJmKLt0IgwltDPcPR2hxbUOeZXbTHycxJE/gjWYEl6EIrY+8ppSjrOSD1hpJcSTO96Rw\nHAc1hu18p7Q+ia504LDRmVEGmj20HDfEANUmIkWXoT0V1WAky3B4CiXXrKV5ZmtI/EJYkU/hOChA\nn/FLDy4vIkU2gnar2rfSiie6jrSKx2mRIXTmg8AAOtQ4THsvksx2ge29XZTM7IrCCGmlaK6OmKFj\nDeyM6V3fPtjHKGWItigheSPmBHW4XPf3/gcfoOB+6HouvvLKywCAZDbBpz+jSbvMUqRc1/bFjRv4\n0Y8Ixl1reeiwxMjVq9fsQeRJIYRBpWF69nQLjiCYafGUi5e/RQtRZ72ONKd7NLMCJcun1KIGpprG\n2dm1Js4wHJaMxkBeSQI4cI6LNPL1lfEt62py8AAffUQblhs334fbYOmT1THKKT2PdrSE8R2CSb//\nJ1vY3gzmah9Ae5ucF8PpJMbNm7RZ0MbA4TExmR1a+D0vSusW0Ggs4eCAFu1Hm3t4+VViAj73/AuP\nzcVr7HXq+QEKPgyMByMsrNABLzcCPYbGsiSxgsGu8rC2RrIXncUWHtzb5GcYQ+Tz91OgtIuPH3hH\ndYplcVSioY11sxBCHtV5KWXrrbJ8hoePaL25+3ADgz5toNqdfav6/tyb38al12nz2+wuHn2XclAh\nk0pKKwRKfn/V7x8vBzGWCfjk9XE8GaL3gDbgwwe3kXD9oi5TW9NkBGC4f+48umdlaJZaHSws0jge\n38vsZtZoYw+EUw085FLDPHaxwrWn7mwIyar/ZhxjuE31dMLzMeJyhs/u3Udvn+Zj4UoMuTykzHJc\nOEtlJhcXniyIfALzncRJnMRJnMRJnMRJ/Brx/2tm6jG9p2O/PfJRO64Dpe3PBsbahvzq61fsOeCr\nL5K33fWbjzDcHPH3Hk/IPM5oM49dY/7MVGmOBCI73RYUix7qUtqmSHlUZGiksKcPRwBFdaLRJdod\nPl3Wa0hTSm93l+oYcwGyC4WQiw+1zpFVVggGcCp/O6OhuaC0KI/YIf+fMD4AcZJgr0cn9v3DBPGQ\njgNb+2PoyqIhz61WitaxzSKFnoPlRTqVjqcxXFtkXKAAnW5b7RrSvHIUBz6/RRo99eVVvPUtKkSu\n1RZgWBRFSAFtqkyjJo0XsLBkxcoQworEzRcjGBC+JcwMumCNk7yPUvDvRYGU9WnyKMDe4D797Lko\nS/rM3kHP6mo5UsKk1d9KtNji4HA0QcYnJ8dImyHK4hkEZ2o67abVVRMGSFJ6/wfDA0wSZjVmA6TZ\n/MXZWgkUVUJP+HBkZSfjwfCp1Ehti1tFqbAaMsyaKbh8+tcGkKLy+CuhKjsfoVAY7pt5gZCzGgG6\ntpA5G6VYXKOMcSaVLfYsS4E85yyeLlCmlaCeb33L5oksyxAy5N6uNy1zcJJM4LBWkCMl+gwFBpnB\njDOMBQBpWBAzB2Iupva1Qs5wS4IE9RZlkfb39nDrFhXAXrp0CW+8/hsAgEH/0A61e7dvYjigjK0B\nrJWH5wVoVuK7rg81L0QkHOwe0LwwjQv8wXcpk/Lt765Btfn9yAympGxFoJbRG1N/mWYT61snhLA2\nPSEkkGm+LwXF5o/SbcIPOGNWFBgOSF8rasQIQ7rO9uZHyDdJp8udXIGzS+NDNVNsP6TT/u33Enz7\nv7w0X/sAoMiRx0ziqTXhcoZzabGD5VWCZhaX6hgOOJOslbUeabYXMRpSJmL/YIC9PbqfTnsJgj8j\ndYlxQPcmvQQxw0mj0RTb2/Su4lmJ0tC8NRpOccj6Y+2FBVw8dREAcGq1g7THumS5QcgCsfOENgWc\nilkJY/XCII+sXMqytBpYSnnWDscYY0sAAqXwzGV6trfv3sEBl4lkjg8nIkTg7jCHukn94bvfzNCu\nstBS2nlaKglR6Vv93HCrMnplWdqfff/JUNjD3jZ2ue9nOgFqLLxpBAsyAloAkjPhcAysbWArQMC2\nUOK+sF6EBmSpBgC9bIi9CWUPRTrEsqI+WdNTzKoSj5HGJ29/n77L9y1x6a6ZIV8i+NtVEknCrGVt\nsMso0MVnLz6xjf/OwXwGQFlwY45trJR0ju0D5tsIVEyXxU4dTkoDaTSeQPNGQx2nmP6SoE3P/Clb\nIWEF89qtJgKGr2ZZQTACAGnkESQnpFXwVkogRUVRDrG2TJPq2dVldLo02S4sHCLboYkjcH14PKjK\nUqHOnXqWHmAyo45SFNpCh8dpID+fmp2XtbC0cBpff4vMWm98egf3NyiNHicGjQ7drzbA7t4et8mD\nzwM2kI6tJxoPxyh4gxh4IeLK5NYUaNZpInIcDwmnfe/c/QJRgzr88y+8Ae7vWFw+ZdP60BkcVqQW\n4kh4joycnwKqRQyUBIFIr4FiShPy4IuPoerMmKop6BFPyCGws00wxsZ2H50OM5eMQYONjuPZFDmL\nnUJr+HzPy0sdeOw5GCiDyZg2GgvdBJrz7tNsCC2qMTHBg+37AIDP7t5FrUFtDEMfMp9fDBFC2s13\nCQPFcFvd8ewGCtJYZmJZSlSiztoAQUibwVk6Qc7jtcgLCxuUWqM0Vd2ZRBEzvOQuYcjK3ypOAcNm\nssJBzjUnSZFiFrMoqxFQ1UWlxtPwiUh0j342MHYSDrygKo2hmiZFC+g4HqHgucGHguZOlmUGk0nl\nNWngssq360u7oLieh50dqguKkxlGz17hzwR45hlSXR70B4gTOnxMpzOsra0DAK5cWEXGptlJkiFn\nCvmTwnEk6i2aX3zfwOGavAvXvoKtHt17I3Aw2aefg+AKVhZIpmFvYJAxezLPdy28XBiDgIUNDRSy\ngucj5WI6oedUZjESVvN3HBeGF8OV5SaGh/SZ4W4PWUzX+cn3b+PzOzSeTr3YxDOvz78h9gSgmMUW\nKIEmi232eznWz5DsQRxfwcP7NN8YoZExVJfmBbqLLGWCI1X4ne0BFnjz2owCpBP6/KSYoNenQ12a\nGbCAPwajDBmzLff3JxiyAf10XOJWnWrdauISziwy5Bf4WFis3PyeHAICLtcu6bK0grVSKjtni2Mi\nmabULIDNauW8ljiuj3qNxmWpgaVlrkM1ObRL7Y1L4LNH1Nd2xzFaLDB8fPbXWtvNJo7VTBkYa1pf\nluVTCVp6oY+Vs5TQqJ1bhlF0P0ooSJ7nhJSQ4khItmJs9+8+RMoyCVprmySRSkJUNcD3buPSmOuE\n0xnO8IG2QIYZn01avkSD15xUpXBYKPfiqS6WllhC45iXpnYlnHB+SPoE5juJkziJkziJkziJk/g1\n4ku2k3k8jmdHqsyIrwwuX6QddT0M8JBTrTu9CUre+xnpQlZlaL9idyz431bbARpsqZCW4hejdo9j\nfseuoZ9KtFNKWIZPrRYi4MK/6fgQRwp7xmr361LbAvfffOMVzCrS2e4epixSWSYT9MYVI07jpRdJ\nW0MJFzX2ARkM+/ji5l1q79oS7t5j9kye2+/9eXn86vk/zQnDkSFeeJFENVdPXcBP3iYYriwzGD5d\npWlmT7Q6K5Dn9D2+KLG7SzBDEEiEPovcFSWmQzoRLvodOHwSchWwtk66MvvDGd5ngVBPOWh26FQf\n1WtAWn1Xhhqn15XrQaLKQALiKVIaAjFESvdTToYwLMI5erQPUwkOOhl29ylb0RsG8CN6z4udLpaX\nqFhyMqlhOuXrlEDluCZcF00u4K7VGihiZjU2a3DdKtOgsD0lRtijyQDK56yKr5Exxa5zOkKtzgzO\nAkjn03oEAMxyjQnDXkKW8CtGTeBAgAkRorB6THBdyxw0rouYT3i5LpDk9BmtgZT94WbGQHjMskwl\n/IBeQEMonK2R6GziH2JYUJ8tDUGPAFBkufX7U56LlCFjEn+dz7cOAGq1GgQTGJI4Qc5UoUZUw3hK\nkEyeFXC5z+iogMNaRL2NXWxskVXPKE0wKStGoYMWa9UIIzDivhHBoMsn3cFggD/7838JAFjsrCJl\n/0SlXNSZoTQcJ1heIkhsOosx6hFEUa/VkObztbHeUGiELH6YZFA59Yt4vIuzZwlmFMLB3ZvvAgDe\nvf4+vvI6i5K6HeQJC1QuhPDB93WwiyymLI/UBjF7BsZTjcUFGou5EChZNyrHzI7vrBBQPkFmDfcR\nDnOax2/eneDRLn3vxe80IN39udoHAGdXTuERC992GpGFu5M4Q8IZziBw0GGiwWhcWO212TRGs07j\nKQodGO5Ho8EQPkNpZZFZPbrMFJiwWGyhHcyqbFR/nUteYAAAIABJREFUjIz7dae9gIShYOQF7nxG\nBfGOTPH8G1cBAN1OxyIS84QSyqITxhgL+5c6tzCfI429f8CBzyzu0PMg7VpS4vQqFdP//ne+gzSl\n8b2xuYF7u5TtHA1j5FxusL3fx+WFJn/XkcWLEsSoBTjDf2x5EMdwP/kLvAJ/Wax3F7HBMLHRDirm\nhDGFzWALc5TtylMNh9vlaYXxPo2PIi8ALr7XWmPGEH1tbwcv8CRvyhiKM/yvLESQjIzUGw1LrinK\nGQQTYRqzIbItzmYaBZezUY21ZTQ683uBfskK6Efsub9HAOA08/PPruO/+iPizMa9fcy4sOP6vUf4\nyafUcTcOJtakUD1G13yc7u8xRLFaE3CZ4pgLF6oy2hRP7gxRGFgBwXmiKLVNhYaBg3abJp1+78Di\n0EoKK5BWr9fwjd+m9v7jP/4PMZnR7//2f/5nGLxLAngr7Q4G3N4333oT/+S/+a8BAMPRGC5DMoPB\nIf7kX5BX1dtv/x267BO1ud17bPP4yzZQ826opCrQ7tCgeP1rb+CT618AALqdFnxWMX/np+8i5oGc\nZRoOwyi+G2L7gCYuUxZYWaYJUIocfsRwpTbWZLPdqMPwAtHf3SCfMQC727etl9J7H/8EW3usCB4X\n+Pqb3wAAvPHVr2HA6fi9Xh9tNv9d47T/rwptpjBTGrz777+L4bhiQwrsPyLGTpkn6HEtz53dDCkz\njl6/chE1Nlc92DuCrL3QR8JQZp4WYHQW9a6DWVqxwCSWFmjwuo4Dw6vCdNpHwbUcWhi4PJEaIeyG\nqNAlerPZE9tWxSiHFRFVElCs/FxCwUE1gcS2rkab3BrOSulZyLo0GmkF85UCjKRgnMYI2GQ4nzkQ\nbGA6SDcARRBIZ6GLWUGT4aHZgVd5PqblkZEyAM2MSAkJV0Vzt9FzXMxYfmM2GVqhxjLVCLmGZJbn\n8NhIWc4SFOwDOJrNkFSbO3lUcecFPuot2nhMDkfIeGEKiwgxL8TSEThgmPtwv2cXO5PnKLkeKfQD\nOyz7h0MMWXRSoQTmKD8AgNf+ow56j/jZDyMsLNABY/vhHUhWVG+0X8DSJYbW01uQPskA6OkaZMJs\nyyBBxEKq3koTRUKwZJaOMUsI4oZYwOl1NsuN+9jZpbmpN/gMQjCV3xNw+L01HBeuoHfleAIK9IzH\nuzUc7s0vG3BqacHCvMsrC9jeZumFg56VhWm169ZcPk5GcFhepiy1hYpSUbDOBuC5Avy4AVFaw3ff\n9wBmVsdpigFLTRwOpphwndSVy+vodun9jw9HiMfU9nv3NnDlN+mQ0F1uQZbzbzSiwLfJBCElSlVB\n6wUyhppnpYHhXui6R0tXGk8RxzSnHg4PMJnSnNdut60QaKveQLtNSYn27hBOnebCLz54By+v0XOr\n1RtWAUYJYWsTpZS/lK33NIfwSBnrY1iL6nCbtGEpdIajkldtpXMADddUkjcZZofUD4ssQ8n3M4sT\nCP7jeLyPLpdXTMQYA03juBVGaNUqtnyKkA94iGP0tmiM3rn5Ce5O+Dp5CMEQ6vkXruCbf/hdAMDq\n6pNrpk5gvpM4iZM4iZM4iZM4iV8jvtTMlBSwTIW8PM7Og4V2Xn72AtyUihXv3vrEwmS/++o1vPIc\n7Q6//96n+PAmFT7vH05g+CQioKy+UgmDVoN2v2cWm8iYITZNcssYMMdyZQaAqbAgQ1cAgHYjQhxX\n/npPjsk4hsMnC0coLC0Q4+TBfYkxFzqWxtjdfi2KcOW5lwAArYVFiIB2yBefew777KMV+TWcf4XS\n9rVuhHar8mzLcMj6J0IaXLhA4oPf+38maPLOf3v3SHPyePycytTcQqaT2S6buQFffesVPMMsh3ar\nBYdZJT95+x18+NEnAIDRcIwRu3bfvPE5tjYJutJ5iYGFP3KsnaJsV6dbt1k7ows4gr3emhHubWwB\nAG58/gE+vk7X/8nHHyGrdItChSkINrq99zaGEzqlDYZj1Jp0Anvpn/zTJzcy6SFnWwYlHYyYtbe9\ntYON+5SZcvwIERctlrIPwzYwD+8/QFhjjZZjNj2NegTJgpNJVtjC5Ww2tf1lNBzh3CplFBzHRzei\nDE6W95Hw6e0wHiJmNl8cF0hYfywIIvhq/pT0NM6hWX+okDnyCqKdSXi1Sg/LrySHMMlTCC5Y9g0Q\ncx8YJ1OkDP9lBhixzlRZGCRppQgTwkwqu4wCkgUws9ygLohUsJcOMWL/uzLJUHLqTjkShhkUsgSM\nN3+WOPQ8TEvORhUJNPt+xVmBpVOkHzM77MGttMmMiyGLpcVpiovPE5zubW/j/Z136H5CaYu1lZLo\ndjrclgIZVywrRwJl1bdhE01FniFju4ygXke/T9kCkcaWLDMZDlDk8803L35LI9M0zrOZhGTWUuY+\nh+mQoLRCfIDu0gUAgPOVDL5hPam9CM9epXvf39tAVj0DN4RgK45x/ABRRJ+J/FWAs4taFABnCB2n\nbjWnIlci5XeVDmLrzxn4Aous85cfLGP/w7W52gcA6+eXsMSsPSGAz2+SZtdomuNrb74GALh18wE8\nn77X84985abTKaB5LNYCCGaXTqepzZzH8QQd1r5L0yOm7DTR2DsgiH48nqHUbI3jlWh16Jn3DrbB\nUw/GPY3ZAX3m9NKSJWXME7UwsKxfCAGX+76oBch47elNUiQVicaQvRMAbD96iM0N0m/KdY60Gn/H\n2HahciDYhulc00Fe0Jox2OxZPSnPc+yCIEDZKbqdx8fbL2LzVczCXxUHB1uIGjQ/Ra6PnLPuWnhQ\nDImGjoDkNcQNfXvPu6ZAo01r3sEsRV4xjJUDl7O+/ixFUKf5dTidImOyRL/YAppd/l4PCZf7JNMZ\nDnq0PjwYbeFne/TzJGtY665+FuPZr74JAFh9Ygu/5M2UIzQ6ETV4d1xaeQNhVEWAw50vbuHZDg3+\n5uopHO7TxqooC7z6IjFkLp5dwKe3Kd37g3c/w0c3COYZTXOYCn7QGdZalFpe7jTx/R+/T78vFEqn\nwmsFJA88Y0oYTjEqDTSZhbXYjnCDJ7154vBwiCVW4IUQ6LLpsSs89Hr0gsMgtMaZ0gBbj2iDMepN\nMByw/5U2cLkeysBBVKPJSEqNnT26n4ODQ7z/3gcAgM8+/RTb21TjgVyjtUif910fM64VEcfkH8xx\nscuniD/9N/8tPGaiCSFRq1P7Hu4KKO5Oy2fr+O65V+zfVNj93/6og3/+v3wPALC7cYAqv1vzFaZs\nijqb5RgOaPNVZDG8iGAgx4/gcT3RpLeDnEd+KGOsnKLf1xY8DGOip3/02QMMxrQoTKYpCj1/ElYU\nUxjJfcRtQAq6zmCUYGfAG/HIoCYZApMBXF7k9/Z30MpoY1iLfMvMCT3niJkDjdGokjGo2UlpOBrb\negkJBVWVBZYCBdcl+dJDxPBTpxHB92khuPzMi7h4/trcbRwNp/B5cckdA8Om2jpzkHF9luP4lgla\nyqr+DijKDFVp32gyRVJtDIVCbnjxKiXySvBRSHQ0TYatVgtFQD+njoKnqf/UsgX0S1q8lJGoykOm\naYYBQ5yLXg1FNr9JbqETC4eEYQTPoTHR6vgQXLO2qEI0WetUTIC6onv7zK3h+dfeAAAs7e3ho3eo\nNjCstxAwrVuW0r7T/s6OVah2XAXNDfBdF1nBoqZKgCsP4Crgcz4QXFhbwjKLEjZ9heHhfPNNoWJo\nyWK3QYmsfh8AsCMU/JREQxvjGmp1erdnTr+J2YDmiMHOXZQ8/7Zba8h4ozQe3MewR2awaX8P0ymz\nUdV9FKNKjsTFZErtS+MjBmeSpBZW07EDnkIRhi4irsmCyfHZj+/P1T4AUJ6G4sVBSomLl6luq4SE\nYO/HNJ0iYiin3ggQM/StixSByx6hyrUK34ejKTyPXkSW52BrVMxmGfb2qQ/mpcThiGvQ4hjtLo2V\nC5fXEHjVxtPg5gckETEaxrjzKW1qFjt1tLvzSyMEroLg8ec6Dg65POHhvTvYYKHLU5dfhN+iw1sa\nT7HfI9hrOD5EypsOo46rlUtM+TC53+/B544X+i5mfXZfWFlHkxX05TEFIIFjosi/JJRST1UzdXBw\ngAVW9q/Xmyi4v2VpDOkzPDc8wO4GreWQLmoeM4Bbq8h57J5dPYOCB/XS2hoGe9Sfy8IgZ8i1HBUo\nBvTulk95CHijmmQKSVk5dkwwHlVrfwMR7xv6WQFT0Za9CF40PyvzBOY7iZM4iZM4iZM4iZP4NeJL\nzUyRNQTvBmFQCs5wQFitknc++Az7t4h98tpXvoKrzxFDQtR9bO9RBmelu4ivv07WAJfPr+H9jygb\n8cMPvsAXm7Rjb4cRnlslCCGdjnDz+g26h87zVjXKkQUcPuV7jgMmxkAJxxY0KsdDWs7/mDzPPfIB\nhEC3SzvqZ3yF3cqqwvOhK78u5eC9t38KANh8+AApZyDqEOhXbtdJivwz8jW6s7mBn/zwHXv9/S16\nJhs3byCacJGkNhjnRwXuOUMsRZZZ8Urzc0Wu8xYTfnT9X9sTiVLqKCUNFx7DTGHgodJWlEZDs35Q\nrenhtdeJwfReOsYC61KFjoshF/5u9SYoGZLttgK0FrjgWCUYskBou+lidZFOukunLiN2mPUoUjiS\nToRpZgBO/gijrd/cPEF+fFzYvzPCPnshxlmKAbN64jxHraTvdQWAig2nJBSzTWbJDIZxMulGUHwa\n9jUgZczPTaLGgnSF1phxQfNBb9+evE3pouYT/HdxeQnn1wlaXVu/gmaLnme7vWjfxVxtNC4Mn1bz\naQrDp+3cK6HZ8sQV0gogutLHjLNs8SxGg7VhFpoNHAyZ6eZ6yDnbsRgIyIhtYOIarjl0KvVdhZTR\nyJ/uf4YpQ71OXUKyUKc2JfuVAWlWYswUV18E0E9x/jMmx6xifDk+6sxAi6WC5JTbkvHRYWjPSz3s\nMqxyYfUcXvoKwUi1zU2cv0JZv/Vz56D4uZk0tQXoAtoW/PZ6B1aTK0szlAy/SWOgGf7RucRsRHOV\nOr1gs71ZniBJ54OIjKiBJZjgexqOS+9npm/gxpCyD/XRJSzVqb8kY4UQdNL2EGPnEWcR1S7ijOYX\nk+1Cs82TMKW1q4mTfRxw4lsZDwmTL4pCoN5kuBgTTLPKi7KJPKaxmCHEI4aioiiyGknzhHCAgjWG\nPOmis1Sxz0pMxxUcKo6EK0PHMjVNWVhh1CQuUFYlILpAxdE2UBixhthklGAwYpZqKTFlrb6sKHD5\nKq031168ACU4U7rYRTKmz9z49AZmfD+RV4MXzK/59j/89/8dusxGXFxYxF3OzvzgL/8tJgOC5K6+\n+R0ES6TTdOnCOp45RWublAKNRmWF4kFZ3cESHs9Dm+M+0gp2hkDGY5Rgt+pdHB9XEr9MqNpqThnz\ndAXotQb2dqg053Caoa2qzK1CyIxCxxVot2k+86I2kpjnV1Mc/Tzoo7dP42aUpjj3wnP03M7WoWN+\n7zsDTG7Rd+1ND9FkUkGn20W8T5nEvJzACemZq6yEL+jdyXwG47BwbzxGf3d77jZ+uaKdUqHk0e+Y\n0tJBBczRz16IT66TsNz9m3fwxisvAgB+7z/4LgI2I0ymM3QXaAFaWWzjd79Jk961y2fx0W0atGun\nVhDww/3gX/9fUKy6/MxVFwjouwKhrCFwqUvEGf08S4GHO1RzkKQakPObHTbqNQsXGimwwFDd74UO\nPmA8/sHaJZT2mhKSF6n+MIVgCNKpR4guETtE+T4019ssL60hY4ZbMj6E3qZOc2V4iMs8IW8FAX7C\ntSLLCw3UQpo0N/cGlnIuSBJ17nZV4aspygr7MYDgjhr4NQQMdTkqASM8cKQAl+PAyBleeoneWzJZ\nwfYOTRTbwxFGTMHejRMcNmjRe8asomQpAkdl6DH84fnLWGKYpr2skPNEqlMFh+UQ8hJYCCtmToF0\nzgUKAIqDXQz3GGp0JTRff5ZkYCgeM21Q+aZKpaykAYSBX9GiC4m8ZPaidKAFG3cWGZqNBj+fEmFE\nk4mSGj1mleRljAtnKa3/G1dfwhqb8S4vnUKtgnx9D5VUgFQK0p2fju37jmXCOI5jIR+4CmMWlnR0\nDs1q3HUjcV7Q/TRqHmoV1OjnEA1WQFcKGW/iQ2jLnJlmAgUzjhwvwENN7/EwG2LKYyUyQMZ1Z0ZI\nVJN7lhdwWaphnOSQ+fx9djKZoLAebAIOL6x+XMBh5p13ECMc8MFmmkExg2vl4gpWK8HHWohrLEfy\n0osv4Sfv0OGnPxjhtVe+QvcZT7GzRWMxnU0Q1miBk0bAY9fmIoshWNKlU/dx5hRtkH0XFtpzUM7l\n7gAA0KVlGgtPQ7Pqs8lDlIrgqsP8pxjsUR3hZr4MwWbF6eEhJLNsG+0SNa7VDB1paxaF68Nj2LMo\nZsi5r8WJsnVv9WZgzcWl8tGI6PNxGSCvKP7JDCvMgHMiF6cuzz+fRvU6ZDWBCGHZXsoB6nwIUTLA\n/i4tsNKpWaZ4oTUyNnPuxYeW8u4FHiqjCWNgN1O9Xt8eZkvjYMabuCBy8ezzNBfX2golH5AWVup4\n7o1nAAD3N29ZuZBCw8p5zBP/0z/9H+G5LOmiFAQzz6fDHiKe53b/6q8w5eX68vl1vP4V2kSM9h7h\n1BL1o2svvGi9H4WUWF4mEdF2q4beAa1n+70ePPYCFdK1cgtSqiOm/a9aFyrYv9QWyq5Eh39VKM9H\nwVDa977/d3h2iZIVS502Pvv+jwEAL798DeevvUB/0F1E0ue/bdZQ48PV1vVPMNqjdx0fHuL0pfPU\nxgsX4PLmbvmShw8eEbP9T//qx1hboIqn/+L3VnDpKj2rTz66i9GE7idXJYSgOW+1IyD4Pr1ijJ3b\nn3EL/uiJbTyB+U7iJE7iJE7iJE7iJH6N+FIzU7Nco5hVGk8KistYhdGVPQ+azRAvfeubAOiEN+bd\n/s7mLlb5Q25ZYj+jTMNhf4RGkz2amh5+7006QcJzcHDrPgDgwbsfo93miv5aiVHOu9xZiYRPLlmR\n2xNHqR1oURXPiiPhwjmCrEt4ty8kZszeSeIMTkrfe355DeBiPC8IcWqVUpvdxUWA/eSSIsPpMyTA\n1qzXEQZ0qpqOJ9jepBPwF++8g9s3KBO30F7GPkML95WLkmGnhpzhAhcZzhKD3R4Xt/5ck+Zl8ylV\nWpsWY7TNNM6mMYqMTlRR6ENxQaWQjtWNKkuDJCHIbHO7h7t3KVsR1H1cYe/EWTLF7kM6edQHQ5gq\nkykyOKyFtHMQo7VKp2onT6BNZfWhkfPnpXIAl73hTG4d5ueJ8aMd7LCX2MLpNdRZUBHKtdmfZhSg\nzJjdJCMLmwaej4BtfeJkhCIjhtokTuGuUD8tfECP6P5XFzvIOYvoex76Q+ovdSlQqxHj7Ktf+zoC\nznRoYyC5qFa6Nbg+C5O6LoSYv+jVUQIlQzJhrQ5wVtNI0LEfQJoW6ILaexZ1NA1d34OAZDi1H2dQ\nfCL0oyMm5t5siLCkz0R+iEOGtUunhXe2CHIfxTk0ZxoSTyHjE7HveOCkBkShUL26cZL/nK7cr47R\naGoFcYs8Q8oXjcaA3qEMVBGXyAz1Kz83yLn4txyFMKzxc2q5i2/91m8CAGphAxtrVAStS2NFDPf2\n9tA7IGityFK4DSYhhCGSKfX5PJ2i26b3+K1vfA1rq5T5SqYTOJzKzYt87rEoHAHJny2FhCMriEdD\nchbOcRUKLoDP3alFdcbbfdRZ82qcagi2cBomBVIWpUzGBiFDRco1YKIY4rjECiMD09kUmssgwtCD\n5PfvKUCUNI6/+ZqGaRIs5ayGcBfnz9o4rg+ZVxYm2hb5a50hi4+sjiqYfTwZQciKrV0gYfILSoOC\ncwdpUcJnwWApSxQlfWZppYOdXRrro8HYIhKu56HPwlRad6A5u5iVJdYv0Ttcv7SM8QGN9XE8Q+R2\n5m5jnM2QHcu4GsGMCGOQszadSLaQMZHo3vARdm4Qq3EyHtkM1O1bdzFmnalnrlzGmfM0f0ghUBRs\ncdZZQsboxHg0xT4jMOtnzjwVTiEgbd+bJ/wwgmILqkQ6SNnn89NH27izRfdw8do1xIyPZ6ZEUgmW\nur611gqaXaiQxlmkFHYeEcP7cOM2Iia/JKWHH/+YyB23YhdbBzRPr374Mf7RSzR3tk83ITJilea9\nCdajitXvIWVY3qs1EEXz69p9qZupEg5KhoX0McFMIUq7GI1Ge9hLK5aUhOFal7fv38MzY+rQ7niE\nBTYmXD53Fjsu1Q2FUYTFJdo01bwIn/wt1V5t3r6FgwuEeY8+voNSM0Udyqb0hFTWhyz0vcpTEkZo\nAPMziGCMlVtwhcQ+bx5/triKhVVKbXbqETRTNB0XiBo0kbW6LQQs8+C6Hs6dpw1Gs1GH5OcwHA1w\nsEP0/OW103D+vT8AADzY3MC9R4Tv7h0OkTDDwykSXOHN5s3Iw16/2gg97tM3L/5NYsT87syRarwx\nmmqNAIhcoDJRy4oUelZ9KEB/SJ85HCZIErrO2rkaXn2ZBppUHm4vUbfcuHOIAbP8Lp5ZwgGz/B5u\nTqDYc1U2E3i8uctLBxOG80pdQlQ1QWWB8hfpQ/ySeHhnB9t7vFFSAQRPqlEUwo9osspNjkJXz/Jo\ng1lojc1H9+lv0wkaEW1GHMeH5oVmGpe4d5c2tWfPnIFkKm5e5DjPNPDpeIqoRf0lqDfghAzteXU4\nXPsjhQ/DBxIjcquqP8+sqI2BtotjipCVgbUp4fKYaLk+fqNOE46jPewdVvThAtmQDjl7vb419l7u\ntlHj9i42BFKGo2dOC+EipdofxBMccB2TFA6U5k1c5kLwZiAvSzJA5WcL3oznRQb9FLVvSgX0XABM\nxyN4vKAsjHyEPXpuuTDQrJKdFYV1L+hGIVjhAsp38K3f+hoAYNSfwGO5lkmc4Nbnn1IbZzOUlXK5\nLrG0SPPT2toqrn/CJrxljtdefRUA8O1vvoWDHVoItqYD6xUI6PkFdGUbSbHPP8sjdWqpLdVeCdeK\n5mq5C9OhuaZ9qQ5vk96DSASuv0fzxWikLbMaQsJzWO7B1bZEwFU5Tp2mZ1b3JarB7oUlJI+VoQ7t\n75dXgWmdYcylCEk5vx3BdDywmwhjAFmtWNLB9g5BmXfubSFgJ4C1c11Irh27d3sbg32eG/LMei26\njkKL6zW7ixEuXyX2+MuvPo9/9WdvAwA2tz5Gq131TeCjd5hVt9zCM1dojDpeioih7K99/XV8yrW7\no2yIpYq9OEecO3cWO9u04c7SFIZlRxzPQ1qZ7hYZQoaLk2SK6ZQ2bmEYYp8hvK3NTWvCvbG7jddi\ncqrodjqIuNxkcXERASv+T4dTfO8v/woA8Mf/yR+jxhsHY8xjXq0V7GcMHptHn8ZVot5so86iwlde\n+ootMXh4fwPPvEz3KcM2DG+KlQEC3p64x1izUdRCxGzaU0tduHwdkZ5CyTVrP3j7Q9zcp/526vQ6\nAo/exYP9Q1x/SH2pXs8x4XKVmY4guIZ1kmpMCmrYYtBFuHh+7jaewHwncRIncRIncRIncRK/Rny5\nop3Q0PZoIY6JdkpbAKuNi71HRBvZ6W9DLxDUda8vcStgTYlHt5Hv0s+XX3wOl16jItDu2jL29mjX\nmvTH+P4P/g4AsK+AzT2uZnPacNj2xPNduFzYLR0HsmJDycfkLO3Je642SgFVUXmgMWA4J3EixB3a\nIdccBa86RWoDpxIhKzWcgH7udNowlQ2FkRBV1kHDehktnjuDmKEsDEbQ8ugEXJ1QMiPgOgxBKXME\nC/wiI8I5YpLCQjmO49jraV2gqGT+8wweq0y6jrCibo5DPmcAsLDSBCobCjdDp03PqR6VWGhTNqQZ\nBXh0k9rkBR6CgE6E/f4WvE06yb3w2hIWuADaFaEV40uLFEwWRV7kmMTzW6387U9vIOG0+6j0UeeC\neOUFqNfoRD6aJQh9OuEJCGjWsRoNBujwZ5qtJUgWiwqEg32GCw82D5AxxLazN8HZVS7Knw2hM8oQ\nSKWwtEr2ILXOKrSiz5ClBGdKRWz7hSkMpmyBU289OTU9jlMEnIGCVkhYmydDBkdRu07XOvA40/Bo\nNkZ/RKfhmhdBcup2PBkjr0QsXQea+3IYOJhMqQ+myQHaC/SOtpIdONxn86yAVKznVQgrRKhVCcVt\n1BkgGOdzVWBZW/OEpwLL7CqSFE2GymUjQG2FmX1xCr9G41J4Bu1VnpNyg2ybTvzhShdOjZ7JinTh\nMkT09ocfW7JMqktUHkGNVgtn1qkPt5o1+AzhXTp7ES8+e4nuoSyQsPdm4Huo0kG61HZ8PSkUomOs\nXIOcPSqNKCCYDKJLYSHEFClS1veJGjU0zrJu2/USH3xI73+cGciqqB0KJWt/GaMxm9Fnzp1u4PXX\nOdPRDmF4TjeegSvpuRapi8OqWPxUhilrheW9ErN4fn/FNI+hdWW35NoMhZIhBqyZtbu3g2dYf+rr\n33gN0qHr//BvPsBf/J8E9yyvdnD2LGV6fU8gqrNu1IU1XLp6mp+btBnCTqeD3/omaXU9uL+FHbax\n+au/yOC6JKB88dkGqvTuuQvnYFi3zSBHqedHM/7gu7+HP/lnf07tTWeWgJAVKRyG8aVUtu1SOKjo\n0mmR20ym0RqKhVJ39/dw8zZlytZPnYLhZ3j9+nWsrlKWuBE18ekmFVi/+fW38Ozly3z/R1UgBDlb\nASrg2Fr4NCuIFwRYPkVj4kop8NN3CTW6t3OA1978bQDAZq+P0c+oBGD9zGm0fIICg4BKZgCglA4E\nE3zcKELI62VjqYUp+/fd/d73Ybq0VqwvdRHwfJYcZnj3EY05JacoxQ63I8QSw+8rF89ggYkKyosg\nGZ6eJ77czZTRgGXw4TGDvopZZsJFTFlFTRcuGosEdQVhE4dTwuA39lO0Q1poHn7yAD/6/CYA4NrV\nC1g/Q6ynL372Od7+0U/oOl4dZ14lEcnuhQsW4pJGoeoSBuR7Zu/HWPzq6dqoBCQn/MqywIzTtFID\n4hi1vGQGTCIyDPs0YXnSgebfO0qhZLbEdDymsS1LAAAgAElEQVRCyXU1vYN9yxabHhPJk8JYlEcb\nDcPMRKkCGJ78F1otBB5912Q2nbs243iMk9J6xgmTwmcs20BaFWffd5BXjy8vAKYSK5Ogd0jf2Vz0\nsHCaFtjNjTG2d6kd9eYYSnCtyukI+9t0od3DITxJAyTLEmxu0e+3thfge8xEcscwFZuvKO2kmukc\nxfxlGvjpF5toNmlTHi7nAJOPCi0QcHszJTDj+oQ4Kay3ZKMRYHmFDgCO4yGNqc9moxEEby6urreR\nLhMcfXiwi+UWbWqCIITH93zqzHm02Qi3NAbC1heWKPJKvTnBpDJhHo5xyJu1N799+oltjNOsQs8g\ntELJ7+78wjUY3uilgxGuj2jCube/gxpPF3EQo8FQl3B8sP4stAL6I7qHSSKsGnPYaaDMaULbyQZ2\nrLuuAwiuL5tMUCqmJBeFXUTytLA1WcZoC1HME1mcYsLCrb7rw+/SxDg1LmJJm6xo4KLJ7RK+h4XT\ntGkt4wOM7xKcHj53GRkbNXuBgy5vMC+dX8LVa7Q5iqczPLhFi9fppQUss+/kztYGnr9GjK9nzqyi\nwWPxYHfL9p/jC1ZZlqSgPkcI4cF3WVncTCw8J4Rr5QeSOEOgjiBcj69tghJjSX9ws5fg3jY/e+gj\nvKI8EjWGMHZTs9ufWT/JdqcLJkBCG4N0yIeQfgxnjZmDwoCRXRwc5Aii+ZcdLY1lXGtprHjt6HCC\n69dpI9BoOnjz6+QiEdaO3CUazRB5Qc/4a2/9Fr79bSr1yLKxlbfwfQ9C0bPq9WOMR7TZPHNmHf/w\n9wmS3dlbxNt/Rx6kH7//EPfu0Bpz4XLTIupaFzi9TrVLSTKD68wvjVAUGRp8YBuN+lbQucSxja0j\nrNSBkoGV4TDGkPkvAHilPUQbAH2WVSiy3L5SYwx2ORFRCxvIWAT3n//p/4rf//3fBwA0m027rpSl\ntg4NaZ6jP6RSiziOLaz8h7/z209so5DSrluR52CHTcS3tnZpkwzgky9u4PMb9E5XT61gjee/C+fO\nY/08PfO9wS76DI/XoxCXl6guTIgSo4Oj+bjODOmz6+v4jZdIEeC9997DQ/7elZVncZo3lXfv3IfP\n9WWvfOMbVlh3//AQ02x+FvgJzHcSJ3ESJ3ESJ3ESJ/FrxJerM/X3HOGO/wunpYULuUTpwIXFNcs+\nKYyBZGn3xSuvwItYIDKdoRxROv76w318eIvsVfK8RHSONCsaq2fRPksnyNJIyMqh+zGWnjmewbR3\nqc1c9bxH7RCwKdWyBNKs8i0zNjuS5SkEp2lrtTpqDYIZ6q0WWlws3u50sMAnad/3MJlQBmI6m2LK\nwoLj8RhJwrL8ZWlPjmVpUDCrTUWBLZgNS/00pfS/MFyhLQzqKQXfZT2grETG4pxCa8tKNDoH2F9P\nGx9ZUmWvXJw+w6exwQiThEXcWg4S9iYLXBfL6/Q8Nu/34Bk6JYQ1YLtHn3n0qI8lzvIMJ1MMWUCy\nKCQ8RrvSQsOR82swDWcFFGe7ZmmKKbOb0ryE5A5Z5gkGAzqSa+ni0nnKRnWaTXh8KoqiGvJxpYGV\nwUuqk9MEEevKpHqGPts7LCwswgvplLa2voZGq8vflSNm1/rDXh8zzraMRmPEDJ8URQn9FNBCkZfQ\nfLL0yhJ1TdmLq2uv4P4tErb7/MZN1NjvaurkWFhhtU1Hw+EM3dmzp3Fnh4gPQ1mQ8zuAsJQIu/QC\n6p0Ae5ILaYVCnZk8eTaG5iLQ0FPQRQVfwuru+A0XKfdxp+EAzvxtzEyJjK8ppGPZggNZItb0DFul\nhsf+bZE2cBj6NFqj6BOUrA7GiNYp01T6Ap5HfemFa1exyBo21y4+i//7X/4f9PsrF4FK+C8d4vJ5\nyqK7JkdZ0O/7/all/bZaLXvPvV7vKNPwhBDwoFi7rEQGUwnBSgOwdYrjSWRTGhNpXCBg25VcauSs\n66VXFNwGZ+unovpTwFWWFKCUtL5p41mOv/4RMYrvPxxje4eFLqEwPaTxcWp5CS+t0bNJp10YQ2UW\nUXN6VE4xR0zSKSRXOmupYQy9n1u3HmI8pvfz2995C1GD7m0a9+Bx6cZg0EeNC9OfvbqOsM6isGMc\ng8aknevH4wSHh3TNS5fPokWSRKgvtNBsErS3uzXFHpeMGC3sPC6lgRNIfubhERlgjnBchbe+Th5w\n/9uf/gvkXFStBXvYAsjLHHlBz813fEh1dP0qc+e6rtXS0gIW4h4Phih5Hep2u1hYoIaV0Ig58/I3\nP/wBrt+g7FsURogiGn8LCwvWDmwwGkNy38+L3K7Z82SmdFkeE7OGzcquLi/hYI/mv42NB7jF0OQ0\nnuDBXdJHe//dn6LWojk1Qw6Hr3N6YRkvvUQZyTRPkHFdR6YVFGcGX3vtFXzzLXp3QmkM/pYEr198\n7Q3UuY3SC60W3IfXv0CPfVl39vZx9jypA/zBv/+dJ7bxS91MCfGrUDP6B2WOTGC1gRXzpIorGlS1\n7jJRLAAIvwPZoUHbNAV0VtXGKKLHA0w7qHwAtd0dHYf1YMxjN1f9k3pKJExKZTdTBhqSU7PtdoRW\nixYRz/fhM1wR1QLU60c/O351zwYZqyV7voOSoTXXc+AHlVCmb+tPpBR2MygE7MIqPYWCTUyV79vB\nbwCIY0bT80Yrcm19R+i5CDhFm2Yp0mOLgLQ7U9c+zDSRKNlEVQYC1U65Xi8wnVL6uFF4Fv3VKsfS\naVosBqMEw11651efu4JunwZjGqfY3WGja6eBvT16Znu727hwhX4fNbWFGueJrDTIGTONswJDHvgF\ntJ24PM9Dt0PPvtZsYbFLE5Tr+vBC2kSUWWJFWBdPr6OQtOlw4gwBSywkMx/9A5qcpVPHYp3qOurt\nRSRcd7Zz4y4OOb0+mUyhy6NOWfVPrUuk2fx1YSpR8HnCcZVAPKP27u5so9+jw8mD3THW65Rqj7oR\n0loFdWmoktXr6wEUw4KTcgbJzy2HQR5zbcMiMCmYXWoECmYvDiZTACxBUfeQVTU/hUbBmw5fGbis\nlhy1m1b0cp7wGyE6rPgtpcGQDyF5niLl+ymUgzoz/lAADgv9FmUGbNJzuPNv/hpnv00Tcv2F88is\nqnaCG3eI5fVoawvf/cPvAgAun13Fxx+TEGGg1tFihep64GAyJOhlmswguA4kSRIUvOnL89xKiTwp\nBFwolqhwRYiE66GKIkfE8J8XhJhymxzfheB3Pu7H8KvauGc7OPMsjY+7HwxRGRo6vkCdJWX8oI6Y\nDb9lMsHdu/Rd12+MkbFQpDYSLtejrl88hYzng8kggWKjvlqonkos2AkcC1+WorQSCHfu3MPyKh+u\nlxro9wm+KcoUITNfR6MJ1k7TIadW9zGu/DC1hwqUSRMDwTWus7HBdEabqXrToCxpLkmLGEsrdLD9\n1u+8hi++oLKSLC/h899qXUDzfKak+3STqjFYP0Mb7rAeYcZQOaSwa5UBbGlIWh7NZVLKo9pjpaF4\nrlWug+omSq0xYxPueHMLB6wgrjwfDovyuo7CYEDzUL1WR52TFaPhwDL7BqOx9b79x//xH+Patavz\nNxGwZT2rp07hP//P/lO6NwNsbFA92jMXz8HnDemLL71g56fpcGw3hpkuoXljCCMxGlI/VJGHrJLH\nyA0cvv/llUXMYuq3rU4Nmg8Q9x88QMEs0YcPH1oJjb3dfSsvkWuN70Sn5m7jCcx3EidxEidxEidx\nEifxa8SXDPM9OQwkmHwGIzUMF0mq4xQDUx6x2wAYzSl1YSBY2FEYAV0VmtO+nj6rjorgibZQFbc+\nfpSwxdkGj2luPCkcR9mTTqFLLHaYhSVqiDgbJR0Fj4Udfc9BkdJOeDyQSPhkNBm4cDmr5SjHimNm\neWZ30XkRQ7AmSVTz0GpTRiRJmxD83IIgQMGQSTJJYPgELHF06nma5NtsNrN6JwCO9GyUgHssi1id\nuo0RUJwhzFMNh6v7Gg2FWp2e68pKiL191ofKPEguCC2LGK06tekrr5zFD/+a4KdHW3u4fIlg24c7\nt7G1Sdf8+jd+F56k09XNz/8tDtgS5sqSgS4qH68nR5qViPm0PZrE8Jgho/PSntrDMIThQvkinYIP\ndeh2O9A5QyNSwq0RPOS4AZZYANMf9CE5y9pdWMONu9Sund4Ily+R5o3XXEb/kE6ovWGCKWuvGV0e\nKz7VyCpBxuLptLQiFWK4Tac9vxFBse7Z9tYmhhMiKUTLLrqnCYKK2sA45cxE6SBhWHZf72IqKSM2\nSycomAVZ9xWSjNoopkCNzdeV8THkrHIcuwjYELPVdBCPeBzPMpR8+o7zGDUmA7iugML8ViSe76Jp\nKLOZ57k9cRamRMjWVDow2D2g+3e9GtoMa9VTH9in/tN7sINdzrL4ykV5iokQMJhyoe6Lb7yG569Q\nofnBxm1w8hj1pTaGXAg8iEvL1Ou0OxiNx/befJ4Pms0mut35nOqllBhW1xBjq5OH1IVkPT/HDeB6\nLPwoEguZeVKhZAun9jJw9TV6xg8+nwAZvcNWcxXnL1Pxbr3RxBefklCkNCkKJhfESQ7D1wxDgasv\n0Hh1ay4OGCYdliOErG+VFoBfn38sagnLIEvyDId7THCYjHD12ov8IHJbxOzChQT9nMwKnDtLvoSe\n7wCmKgHQltDjBy5qnA3eeNhHo0nv4bkX1y1D1xFd60l3/pk2dvc5oxhn8H1mL5YzOAyDCuk8FblH\n58CY7Y2uvvgCfvQjYqFLLex1pJSW0YbyyCNPaGHhswIFcvbg87QHx62EeCWCOmVqTFEiY+auNBJx\nws/BEYiYGZeZAgc8oW3cvw2PmazCddFlWPvZSxdw9vT8WRtjjC1kl66L568RGaDQGhe5uPyt3/wN\nDEbUZ/zQt2tVkWaYTTkD5brYeEDz5fWPf2Zh2aXTKzg4IG2pHAKKs1R5nuHRBn3ecZRdr/7sz/8c\nPtv2xHFsM8PaSIs+hI062gtLc7fx37nNFISxqsUQEoLZJNIY4Lg5L39GCA1R1eTAoDxGK64gQmPU\nEW5nhN0aCaOPbZOOwXyPsQwNnm67AWtyqhQwm9BkNxyP4FfUbNdBWdWHQOD2HcKGXdezMgJKqWOS\nAq6FPpM0QcrpyVJrJDwYJtMpDge0CKZpYtkqaV7iM2YLTqVjjZ0zIWB1O42Ze/AL5SNj2LCcJhZa\nVK60Qp1SCmj+TJ6VkLyxKxLA5UlgoVtDFLLpa8fFsM9sOL+JRpPVvssZPMlGuFGJ518htsyP/+YB\nrn9B2HejE2GRDa0fbt7Ch+8SG6S/17MwnJ7WILmGZZ7I8hIzrkUajycwPEFFLkErAAmG56yUmxUa\nBcOqRigrSCsdF9KnxVx5TTQWK+XytoWsO8tr8NdJ2uPt7/0rrJ4mVknQWMCU09OlLqzaehLHFsIV\nQsJ1qE8FfuPIw2yOiLwAQ77m5bVn0ODakuFwAMUL39nVDlRAn/HDLrRDP5eDArOInskIMSqJcscI\nRIusYu46cNhTTxttBVRbnQh1j55PtzuB5g7U39+HBPWHKJRWEVC4wRHslZa2JmSeyJIZ3Eo6QtPC\nAwCmNKgxFJubFIchLWQdRyBgs2Wn1BBsgLzm+0gfUh3FZ//7X6D2KsEbK68+h2++/lX6Ml9B8yYx\nNxmyhMZ9p9tBt0XsyiSJsbND7MjpbGrHt+/78LyjA8q8840UCkcFThqSx4rvK9tncxlD85gvCtcy\nOIO2hxFvxMfxAbrr9M5rbRfjLRbJLFLsbT0AAAx8D/mU5pG2IzBi9XQoBSl44xbkWD3LJuiugxEz\nWQsnRjzleTxQ8Pz51R6VceAxFOWLAPt82Dy9toxnLvJYiQDFcLqUCqNxVeOYIoyq+dRDxtCucgwE\nP6tc5zjo03h69PAR/sE/eAsA8PLLz8Lzq7oqx/qttpsBTq8t8r0pKBaA9kQAh+e5MiusWe488Tu/\n/R2snaPD4et33sAXbGo/OOhbw28plWWJSwhbM2W0sdIXxpe27MN1Xcv+M1JYHz1RahQZe0Vqx64f\nUhq7xoynE1TnslIY5MwebkYhLl6kw54AMGapFFtc9itiMpnYDSD9l77L9Rzb210lsLRIG9u8zKGZ\nuam8GlaW6FBqYBDx/OE7CkMuf3hw7y6GXCqytr6GjS3qq72DPs6/eAUAMBxPkbELQpqVWF+njeFs\nFmNri5i7Qgir4i8BfPD+e09sWxUnMN9JnMRJnMRJnMRJnMSvEV9qZsqR8nE47RdlRo4VhRNboLJI\nMI/9meb/N8dtaYyxlf6C8Dn6DCSOn/Z+kdfeYxL6x29R4LFM1ZNCSWGzaTDGev/df7CFnV1KQ166\n+qz9/GyWQOuqcFE9liGSfIwkuK/Ksml7f2maYeMRsWo830e7Tbv60XhmTxzddge3KxueoHaMZXIk\n4Akx/576YD+B4VRp4PlQnGEpUACsYSNkbk9UQeAiZJhRl8AsphP+YJRiwnYjRcl/D+Bgb2zbnRVj\nwogAFMUIhq12Xnurg3jC6ezQheNTEXBS3MXCKv3+4YMUMARdTGceoOYvztbCsSeYne19LLDDOZqh\n9YkTykHAYo+BAfxKwNONUHJGxlUhlEufcYMaFOusSL8F6TEbtdFCwO/21a+9iYX18wCAOJ4gYzh3\nFscYs8ej63qIfIKZlHLgqKNMTXWynCfOPXMNzz1HEHSrUcdhnzIQYpqhtUjPLRcBxjN+/mNpPc+k\nbxC7bDOiAVPNIqWCYHuNXOaIGFqXKKwYYqZnts9k5QDTMb33aTpDzp/X0HAqWwmhELNfodMNkeeT\nudvoaA3XiqmOrXZOs1GH5PebCw3D0M7AlMiY3bRUb0KkrFcUhZZpKLYP0X+XWE+ecaAW6MSs2iG8\nBpMxBgOUScW+TOHxe0+z7LGppMpMGWPsvOi6LobD4VztM9qgFlA/miY9KLZRkc4xmCPLkXEZhHa1\ntWMphYFoMoNaFqh16R+aSy4Ot2isJNkEySH1wTIvAIa+V5e6KDqU2ctmsH6rwkhEEY2V0DNIGDru\nrtbgO0RkSPUMKhjN1T4AcHMXDpMdPOWi7dH1z5+RqHGmX+vEZryzorAw+JWrZ7C3QxDPYf8SOuwn\nOBg+sixV5QT46Tsk7Lm21sXX36IsceA5EIIL+ssYgi1wGkEDK13Wf0tyPDqg+ffc2TUEfA8pxlAV\nDX2OmM1mmEyoX4dRiG6X+tSod0hinaBlseo7UgFOpUVmjCUVoebAD6gvO8pBwD8LV6DgLLrJC5iC\nSSKlA58zvZ6S6DJjrtFqoc12bQtLi2gy23RlZRWn2BZKKWVZfvPEeDy22VfP8+BWSfRja22e5xbd\nKMrcbgWKQmNWsPWRKFDkNLaWVzpYZ//alZUOhixmtrDaw+AviADyyUfX8SxrSN298xAbD4kEVKs3\nLbt+lqYQnP0URW51pqQpsbe9OXcbv9TNVHq4+UTfqb+3b7F1PUeVS+LnJBaqS0oYCMukExZCwC81\nKj6+4TK/9N7E/8veewVdll33fb+9T7z5Sx2+6ckBmAEmIAiMMkkwSAxiiRQpykVRMlWmZLnsctnv\nLvnBD44q2SW5bFnJclGUpZIVKFmkCJEUAUIioBnEASb3zPR0/PLNJ+7th73OvrcHmOnbbnn4cv4P\nwO075zt3n312WHut9f+vuwjzzWZzOp1GVLPiWOqZobQPV5ydTVBiJC4WS69irFC3sR2bUF0Uhr5j\nrDVeYb0oC6qyyVnSngGllMI0BYLjgFDorArFnijDYmov23A3Dsok6VI2OS9zI0kNUFVuULo2Whqb\nLQgV3Y6EhI4Dbt5wi0ycKqrm9YQGk4lC9qxAiwGSVZpMclW0DgiQuHkAsSgY6yAnl2LCYRxz/pLb\nXJ54pkccS55RXIPeXHXZsqpBNV8s6CycodSJNXVTbDtJSJvQjDUEsYhJqth/jjp9oia/TBWe4dhN\nelTCyFtMTrCSH/Tkk49h5Zobtw4pZLbfPJpiQnfP88NdElmJ6rq6bcm+m3EaD2LO77pNYT49ZWqd\nW1x3LYvC5UvEqcaISv0sO/ahTHTNMHSbWpyETV1kZouCyhVvpNNLOV06o0ATERyLW5+YQDb3WTVh\nuRCRxzz3ys+WCi1JRzqJSeV7bQy12Tx80u8PsCILYMqKSCyJveEIcN/PbIUWwcRqWYOwiY5mM3pd\n17altn6u9DtDf89bX36ZvCMr706CHrnPJ+OrGNm8sjpnPl9Ie/qetm9q2BOK+mQ69mOjNpbR1mZF\ncl1oppGTKMkWrl29fhfbCD/aDCWGr9KWyjbh04CoI+Mlt8Spuz7taL+BR1GXqlHjLuekPqYfEqdS\nZzROOc1cmsL+9oBHtpwxcrZMmVduIyon24xGLndJF6dki82FEKeTGaGMwW7S9aGu4bBPljXPtVIB\nr6whkTqTn/rUx/j6V52i9huvv8ajtQu3vvzKq3z3d7t6cNOTBfOxW6M//QM/xGDo1sq6yv2aa4zy\nVSrCMCQQ4+XqtXd4523HRNsa9YmlQoM1ytcT3ASHBwe89Lo7zLz69mvMhHXa2x750Jjbp5rw08r5\nEOgAJWFcE6xY3CZQnl2IhUZZN9QhoRShHyZDdvecMXJx7zzntp0BNdga0ZdDwmAwoCd1/dI48bIm\nSZzcVV7YMlv63C6L9Tm1tSm8kZUksa9Ba2y8ysypDWXu5lBVlURxwyTu0JGctX63wyR3/bazt8/8\nTOr0fe5z/P1/6CRLDg4OOT2TnKze0EsJ9ftDhkMJLy7nlLkz1qzSpMnmJlIb5mvRokWLFi1atLgH\nfKCeqa9+9p+uFf1Z+w/v56xSq4uayzSKviSoaqz35liMD2PVVjO3tf/L221o+647vj/uJv38tctX\nvChalldcFrfi1evHXi/lyvUjRpJkbe0qiRyUP4mEYejDAIBn8xmzSpRXWnkLf7nMOZNkvCRJvC7V\nfJ5xeuw8N3FkePxRx5woy5LjE/e9uoswX5hqOj3nLl9MSiYn7jS8mJUUuXgfophGcipblhyIttRs\nqiiEKfT25TlLqUNXGwNSqZtakxVyCkwTpjNJ6qxLAu08OHGsCeQ0pjTeC0ZQE6euPaNzlkg8U7kx\nBHbzZ7RYH3qt0ExEn2bYT4gbj4kOPDOqMjUiAUM3iojkZKxURClhXh1UxOKxCmzpQ839TsBg6Nhb\nudVk4q4zNmAuYpUYTV9CRcoq6kbgdm28BErdTbSWm6dvMui5fh5nxyzlHWXVBC3lUgKTEsmJdjGf\nU8rJuChzSjl57+3usiXJoWfLifPuAISKmbBUdZgQzuRkHBf05cRcVDWVhKPqslqdwpUiFW9qrCyV\nMAdjo0Hfue5gA510OD5wHrfKWvrCSup2ElcCCOj2hqTiSU7KE5DyI7N6SSz1EMtSo0X0tVQGlYqX\nmMJ7oE6unzG/JaHkqHZllHAc4u1t1z9xlHA4PpQ29MmEXTifjekNnFcjSTt39N43yPKxZ/+GYWdV\nx00F1Fnj0bUI0Y2qNOTLxh1c+/qKNtDk8v7TQe2Yb0AQRBRy2q+rmiARb/pyyqJsajDCg/e5d/Lh\nxyOyM7fezRcPM5X1TocLsp4rAVJlS2bZ5p6pebYklsRuW6+89aEx5HkjUlpTlCttQquaclcRHxHW\n2BuvX+bqW28BsNPbJbZuLNy68jZPPe5q0vUHIZPpif/tJgIQBIpCyBFFMfWhqJ1zIx9Ky8uM2aLx\nGOeeZbsJyqriQIQrb926RWfoPPPxoOe9P3Vde/KQVsaPEaWUZ4gqhY9+dDodH/KL0tgzw21laFK+\n+8Mtzl9w3rpz2+fZEuZxfzRCC7OvE8b0G/Vja72GYhiG1PXmocwoinzSvNLa79NhqH0/K619xCZc\nY0SqEK8ZabOYvmiZBWFEnjWs35JaPPk7wx4/8H1OF+7g+JCvfu3r7ppsyZ543Ibbe9x3n9P22t+/\nyGi0JY9YMREtuPFkwuIuvKgfqDHlxcjeDeX/hxXFTP7pr1ktMBrHFgKIlPK5ImVdEkmnVwTMqsL/\nxbe/690oq22GMi84PXYTcr7IyZeN4vHUD8SqrLhwvjG4FsxmzUQtvIGnlfaDydoV08JNom9nkeIH\na5xEvtZTvz+kI3XFRv2O3wSLovQTDNTGaWHH4wkd2djjqAOR0FepG9FzAiy5WBeLrCZbNmzLgEAY\nMsusJpZ3qEIFoqRe1XB4JCw2rC+QilVeZDQKNUHYsDlZsVm0Jkrd9aMdS7cv1yxC76reBC5M6j7X\nlWXehLqWhVfNtcZ4WnFZ5PREkDUMQx9mUFr7vIJOHBLJs4e2oK6FQba1wzkRH3znaOEp7dZYb9T0\nulsoeVeV0ZTC4Op0+r7QqjHGi9BtgunsFpevNSKWC7QSYz1R1CL+OF7mq/BJpejI4tyNU47HjqnV\nTSNSGQ+7nRGFhCyrombU2ZH+rL2w4zLL6Iubvh+NsCKMuB2m3ipe2pxSBEiV0aQyrtOhhg0FLQGu\nXbvO7MStOaaq2ErdnDMar8IdlwYrrMYiL8lFwLMIDctGJDHSXlE5jywTyb9bqMrn+lVqiZLQoamN\nz0dUKC+MmOf5bWtAUzjYmppMDAOrUh+6vxPm8yvEkpeUhH1sInkxTDESbjVRQRXIHAoi4tRt1NVC\noetmHPWIEkcB/54/NGKr6wQwX//aIYuJpBHooJmi5FnGPHBzdNAt+OhTzhAcDCrefNOxFZdmwOUD\n1zfx6IRnvtu1p9evfW7XJsjL0r/zbDlhKRICxtR0mrloKxIJXWkdeNkDyL2i/UMP7nsjopN2WMxd\nn3S7PXZ2XfvPxse+Vp21Gt3UbAzx9fvKsvRrZW/UYSCGj60Ny+YdUuMtrg1g6srVqQT6gz47F9y7\nMIH2LLwwirwgZ56tmHHGGJ8jloSBHzudTpduT2QqkpRUcjpDpQlkjw2T2PehM74kPSGMfL8pA1by\nC+Mo8gZUnucu/WRDDIbD2x0FnvltV8zBsqLZ04Ig8GuPMWZlWOnQ38fadftgZZRhLfvC8P6Zn/4j\nPPmkM5aLPPcSJL3hNttNWHPQ9/eP4wfd5rEAACAASURBVNBXD5lOJkymm+f3tWG+Fi1atGjRokWL\ne8AH6pmy4SYy+++27xrtp9WfGmvJpKaXDTU+A1ZprFinWVlh5USjCNg8rnhvsFXOTFgFaafHnogD\nvqZu+OQ6aw1zYcAUZenZa0EY+/CSY/ismtsIgSq+VWDU/7Zdefea+mFKGT7ylEv+TKOAl19xCZNV\nZXwipRPZ3Oz5apWQy+kkjAyJ1PQaRYk/zZhaMZ+702FvNyWQ77GGSthSoVZ0m8TDOKKWEFJpLWMJ\nq80WJcOueNui2Ce7F1lO5duwclZonaAbfa3c0JDbtNYU+eYJ6EVR0hcvTFVbMvH4TCZzdrZc6Keo\nKyo5iZra0ll3eXvXfOYU+YAwjH0pA4X1JVWuX3mVQPRvdnbuZyFtnpcBcde9wyBIKGW813lGJCKi\nQagplpU874JicRelVgh8omWkA6xqNI9SqqV4hSy+lmIaxSSSgKzjmLQjTMbuELOQBGEdNnmu1BH0\nxGNlqoxKSsIEdJhLX0UKOjI2BsOYvJnqtSaNJdGfBCXh+qVZ+hIfm2ByNqabNuFRKOV3CwyheJqC\n4wnlTEJZHUNyyXkFgvEc4TWwJCDW4hlMLDOk5plaeI95EAZEUjvNmMozrJRSzERrLssy7w1WCu9F\nUIEikvZcuHBhpd9zB5iZRkmJn0UxwyxlIqiUQJb2UAUkynlN94ZP0VUurDOtjllmLvRWVwHd7oMA\nPPTsQ3QCx4C78upn6ElZDm00WkoyqbDjUxme++guYeTClWfLimtHIgTby7l1Q8L7r1VMj93fPvRY\nxMUnNj/DF1VJKZ4RW67C75gS0Q1FBwGNbF8crTxoWmvPequqqqlEQ6lKCvH49Lf7FFLz0y6h2SeM\nqUFq5GEUOmpCu8aztbOyREldxyiIXQY4gFqF3zdBGBiMeDW3d8+hGz3CUPtE9jiOKXJpZ3/kSQJx\nHHvyQlVl5FlTgiz2O16cRH6815VBi1hlv7/ta5YGOiJs9gNT07CDVBBiG50yLEr6bblcstgwHA3Q\n7XT8rhuGoe/D9fq4LvqyEtFuvGDW2ts02UzzPY7UAdDtdlkum3JKFakQfx6+/yIP7juiTRAGq1J1\nBH6/tNayWCykP0PyQkQ7gz4Xd4cbP+MHK9q5Uee/hwvYrptDllzixFW9JplgQcu/c4wPC6l1sc//\nnxF3uoTysmtjKSSnoqprz7ay1nIiQprWrijtxljPMFg3bmxz4bvwXkaVMdYziMAQSBHe5WLBWNzk\nYZywki/VGzMzNLFXXc/yBZGE2PqD0OfslGVB1GnyXwJqERBMIkss4noYA+JOTSLlGSlZWREJi2qX\nnheiDIIQK/1q6pCVqoOlKJpiv4qGoGhq5TsxCMHqzQ2NujaexVGjmEzdJF3MMx+a6fdSkdxwuWtN\nTttiOcMGDa1fETS15MzKGARFLGKby/mMhagND7bPU0dSBFglWNEcsEZRihG6mM2QVBvnkpZwWFlm\n2HLzxa3f67EUirHSa7l6QchQ8nfyrCASGYzKAhLqUNqwL3lAYZhSCdPGYkhit8jXWqEkdKhCi5Fw\n7dZgm+OZC70FVUZHwiFLU2FkbPSSHpH0Z1lb5hK2KfOaIFwXt3x/9Dpd+nFjFNerTUEpQhmsalGz\nJTXqju2CWyJp0O/GKBGePa1rBl3Xzmk1ZxHK+Fc1qmpYisol9QBYS9lIKSjlP+d57vs5STpexFcF\nhkA2i6OjI7LlZgrhukqYnLm+GWzvEitn4ARB11dYiMMuaSQ5W2HAyczJOpwUb1IYGdeTnGDmhCKP\nk/vpdp+WX1C+4G2oIpRYl6Otc3z3D/8IAB214CsvfNa1YWufUMLpJrSkAwk7qoTpsevLF48XXHtn\n87nYHwy8eKMpjX9vdbmqZ6isopZN3mpDt+fmULfXoZbDTG1qMnnesipREhZM48Rn1Ooq8Ru4sZXP\nxVRBgBapF6UsVgwfayuvC6Jt4HNPwzD2YbtNYG3thZ6pNX2RXKnrko6MX2MMsbBR18NrQRB48WCt\njN9j4jj2uVRaO8ke1z/aV6Soa0sqbLi6Nn6vSpLY7zfG1P57ay2pjNk4jL0Bsgmqul4LySl8eWlr\nMXa1/zUCoc2e0aAJz9V17fdLpZTfL7Ns6dmd0+mUSFiz/W6PjrQ5UJa8ap5lTbxba5Qw1LXFCyTb\nqqa6C8dLG+Zr0aJFixYtWrS4B6hNmSMtWrRo0aJFixYtvhWtZ6pFixYtWrRo0eIe0BpTLVq0aNGi\nRYsW94DWmGrRokWLFi1atLgHtMZUixYtWrRo0aLFPaA1plq0aNGiRYsWLe4BrTHVokWLFi1atGhx\nD2iNqRYtWrRo0aJFi3tAa0y1aNGiRYsWLVrcA1pjqkWLFi1atGjR4h7QGlMtWrRo0aJFixb3gNaY\natGiRYsWLVq0uAe0xlSLFi1atGjRosU9oDWmWrRo0aJFixYt7gGtMdWiRYsWLVq0aHEPaI2pFi1a\ntGjRokWLe0BrTLVo0aJFixYtWtwDwg/yx14Ca0wNgLbKW3JxoHntGy8D8Mt/4//g61/+GgDf/+nv\n52f//T8OwAOPP0op19cYUO6zApR1ny0Ks0lDrJXr3wdq9QO1XPi0br58bwRhxxojrbAGdNU0bu3e\noOQBFAoduJ4YDkakUQRAWZYEQQDA/ZceZnfrHACPPPgAhXLXHJ1maONe4Whnl/F0DsDrr3+TshwD\n8MCDD2CqRJpQkefumjhJ6HZTwPXnAw9+GIC/9r/8hfd9xv/uv/8Ldmd7B4CL+/vcuHYGwNUrt0gi\n9zshQ8pa2rgck3Z7AOyc32drd8td0zni2o03AFgsMjod1xad5NR66tplF6AWAFT1AoPry8oaDIG0\nKG6GAhqLNq6jlQGs8q+hGXf/w3/29+74DpNkYJO4K22bY2zu7mM3GV0bjUCUDCWlFLYZjxaUcmNB\na43WWr63a9dY/7cAfqwBYeiuL4rijs/4P/6jn7ahdn243RmS6QyASkGcuPvkdUZPxQB0dI8F7r0s\n6iWJde80jrrkdgJALxigZJKeBiVVLc9aZByfur+dmVNs6Po2oI+WyRWoDnHg/iCsA7b6uwAkcUCt\ncrm+S1W4tv3HP/qX7viMj31vYmcLd8+yhDx3fVWXil6v49qcJBRT1+jQRH78PPbwA8ymbuwVJSDv\n5eDwFqF2P10scmKZr3GSUlSuD3UMgbyWKNDoyPXz8emCLHP/4eKFc5ydubkTJRD33f1PTxfkS3f/\n5eT93+Pvfv6P2SB3v9/r9TDSf6bOuTF1/f1rv/Mys2kBwCef3WV/q+/aaxI6sZuvVZgxr9w7rOe1\nH19Rr0+t3Huez2F24q7pD4aowP3usqhZLFw/zaczytz1X6fTI+26Z6rrjLJwczfQik7Xjan/9D/6\n3Tu+w6+8fWZV4NpZV4Ze4v72V/723+TylTcB+E/+i/+c+85fAOAf/l9/j5defAmAX/wzv8jOebdu\nlrBa098DCm6bW3eDZn4296llXj7zwOCON/ylv/45a8xqn1Ayvpr530DLSqdR79pOjLTB+PVDweoq\npTGy21qlV3unun0HXH+G22D8BuvXWmuhljX1r/2577/jM85nmf3iF58H4Pnnv0S2LOTWyvd5bYxf\nz5Ik5uLF8+5v5zO2Zc+J44jl3O1hy8kYK8+YJCFV5e7Z7/VJEzdu37p6hTR1e8sD5/eZZUvX/iRm\nMBgBcHx0Sr83ACBNEw4PbgFw/wP7fNd3fRKA0dbwjs/YeqZatGjRokWLFi3uAR+oZwrWPDJKQe0s\n23/1m7/N3/3bfweAa6+/ia6ddfrr/+Sf8cYrrwHwUz/3M/x7n/4+ALrDHnnpLHmrNWj1Lfd/PzT2\n92ZnEEVwF4eVIAj8CUirgK6cgOu6oijFGq8tpnZ2bBAF1LU7GSsNUeKurwwsc3ciP3f+IodXrwGw\nnBww2HvA3T8YkOXuVBilCdOFOxmXtUGLV2s6ndFJxNtVZeSFO7HmxQyFO/3nVcbZ8elGzze9kTOU\n04/tBQw62wAM9gqy3Fn9mSk5Ojxx7c2W7Mnp8Pj4BJQbcmknZnrknvXsNGdry32+eP/DVMY9k1Uz\nqsqd3gM1YVFfd/3HHCXePKUitJywlK1R4j2ypsaKZwqr7ngqXUcSx0Rh5P+t1tyg73V4u4Of81uv\nXj/Jrnmp1kflujfq3dc2WPdeGbN5G1TZZSlev25YUyXub5dlhlJDwHlz5uFc7q9YanE76S7GuvE1\nL6bkobyvWtFhH4DCTJmcuvFQzWpuHbqxPNq/SJG7eV+VFaG8o+FWQGhk3gShP53XqiCv3H2Uqaiy\nzd/jViekF7t25iVMlm5sLJYVi8K1eTKfk8auD5NORBq58Tm1R8Q997e7/S3KTDwrKiEK3TV5HjCS\n020aRJxO3RyygSGOnAdl2O8ThO4+F2Yli4VbA4bDEd3UtWc8mRDU7ppExZRVvtHzTedTktqduuNO\ngAplvGhDmrpnum9/m5fPrkt7FYF4yUIstXJtqYMKq11blAmIxMNcKzg4PAYgCkZQimdvUdAbOQ/X\ndHFGPhPPoY5Bpo0OFLVx3yepIpZ+revKez43glrNiDgIyWfuvY2PTnhDPFC/8tf+Bo898ggAv/2b\nv8XTzz0NQG+7T67ce9Mq2mhv+P+K2+alAmU3/y2tVpGW9SYqu3ZfpQhkHfiW+Iusecv5wl/f6Xb8\ntmgx/sZmbZ1SltW6uL6wvWsZWTXN3P6l2ny9qeua1159HYDP/s7nmcl7DKPUr2/L5dJ7pnZ3d+j1\nnQc7DAL29926kmU5ceLG0ttvv0HjvDt/bpeeRFqUsVhx9L36+mv0B87r9OrWNmcTt54dT+YMB0P/\nMI8//gQAb15+k6OjQwC+4zs+xtNPPwXAaKu59r3xgRtTHkpRi+v3yuW30dKhcRxTLNziGSjFS19/\nEYB33n6bV77qPv/0L/wc9z/6EABFbajl7Vul73ZPuzMsm1pdAETaEnr3KkTytgMbgIRVrFIuFgD0\n+wmdrnsNSdJne+cS4AyP4xO3kMVRl6JwC5OyFUO5/2I+BuM2uLRzgZuXncGlNOxsOSNnZ3eXbCHN\n0RHzpdxHadLUGTD5yYJqvtzo+WpjePOtt9znuqaUGXtwcsMvpHFnRH/H3btrByAL++7WgIMj50Lt\n5X3q2k2W+y5d4PjEGV+H1wv2dpzxFSXb2GAPgEqNycX4Lrm+mshKo1QTVl0ZHsaYZo3BWnU3thQ6\nCImS5v30mc7O5EaG9x5g3gG+wS+o1XUWfPRY6duMvnU3f7PIrIf1lFJrhtj7uOm/DUoqZnM3dnSQ\nURsxxPMcE7pn16rASkjZGChls47CgLo5FNmEJbIp6xyj3fgqw5K8dAMvz3tYCQvm04zJmRgU/T4d\nMWR2k4ilHDYqE6KixribUJZu3uhSsZDw1UYIKyIxEgOjSboSnis1eSnGWq3WwrcFyrg2HM0WdMSg\nLswZqnDXaGvQMneDjmFpnAE1LWqW8rd5WaMz91zj5Zhu4tofERDLwSbLC5LUjefdsEsUufmytwWz\n2WyjxyuqmnzhrrW6pr8j4VNtCSO3tp7b7fKaDKPZ0lKHW9I3EbW010agcAYUJqBuDAEdeKNWWUug\nJLRu9SqaXeMPHkk3ZrkopV9L0o774TQNqCv3vTEhWm8+GRWKZnqrIKCUcFgnCdjfdpvklZde5LWv\nfgmAnfMXeOihB6T9+PmkAP3vem94nzbfxVREW7NmqCisaea09WvAbWG7NaNGAUi4rcwWfn3odWK0\nHHqNtf6zWyRXfbK+XL1nm+23+12FvovNtqprDg+PADg6PMFaSWcILAvZ78uy9KHD5TInEgN8f3+f\nbtcZQXmec+voJgDj5RnDvltXOD1lULg5FBi49c4Nd588I+66efHWwQ2mE7fOhSQcZm7PSdOUk2P3\nOY5Tv28slhll2SQX3RltmK9FixYtWrRo0eIe8IF6ptbO41hrCeWU9vO/8PP0Os6q/Ct/6X8mlhNQ\noDWpJEkW84z/5x//MwBeevll/sjP/hQA3/vp76e37VxwhbXYJgFZrUI7lpUjY/10UitvpL+nx9L6\nFLfmCd4foVY+7GhMBXKS0ijSyLkhgyBBcuV47EKPx564CMDXXpn4BumgBuus4tligeq4Z+xtddCS\nXLecnDCbOpfktC4BZ9XnxZiTM3ef0dYAY93348kpgU58W+dzl1AaxX10sNlQKG2BkrDF5etXOLzh\nThudJGH/QfccqY1QHXdqzHSHsYQBzve36A8lhJTV5JKhfHLtGv2+Cxu8884VYjmKDkcpaerGiFU9\nLm59yF2/NEzzA+lws/bylM+VNNZSyz+sWSV1boLKKiJJiN+//xI3brgwya0bV9H6ds8QNCG25vv3\nOZ+se52a/7fKJysbjR8735J8qr/1vuueqbquffLpJjAqp9t179HENbUkgmuVouRZyrKkE7j3UgGl\nfN+xXarAeUQMU2wh7ygOWZTOm5rnBUXtTpyL0hDIE0/PTul03BjsdCq25L0nHUUlHrE03KHCeQNr\nDLV4JOsiQqnNlyyb4GevMaXv/jhUxB33j1AHqFrJM1r62rUnUDHThfM6FSYjkXBhkqTMzdK3rZJw\nfaEVZVjJ9RWh/FZhcooqkN8Nia1b55S1xKnrh240JA7c6bkyBUl/NUffD1qnnM1cG2eLKecjF7Yf\nDFJqCZXHYeUT5k/HNSe583wTblNn7l1RnVHI+wkrqEt3eg9Cw7Dn+iObGZR2fa+DkEKiCuiAQDwI\nnW7kl8jpJMfaUPoempU/CAJU4+HaAApLIF6MyhiuHshcPL5BXzz6O9t7XD9w60GZL/iNX/8NAOZZ\nxQ/9yB8GIEqCDQkk/25wN14wZa0P1TU+KPdJeW+UC/k119x+8yYNZT4e+3U0DgIqWfcVapVsrtT6\nJnz7lnanFIbbXFf2XWnw7w9TG++NMlZT5G789PqJ99bmRUYke0uv16Us3b4xnY49ucYYQ7Z041MZ\nsKV7xmw8J6nEc24U+32XsH5iJ6RCYIgHAxbijQpM6ElJdV1y9foVAJI4IUmbcVveVUTjAzem1v9R\nN4tbN+HDEpt89uMf46svOJdtYDSJsGWSNMZU7gW89dKr/JX/5i8C8MIXnueP/+mfB+CJj354xfir\na6y8GBfDlo/rbk29MqbeC+a2AbOBMRXGxBK6yPOlHyh1ZbFG2C22ZikDJY3OMRam08nJmPpMQiPF\n2DNCwigEMXauH55weCohmSJjuXCbTnl6Qi15LMvlhFBm2NZom0oG2Xw+ZTRyxoy1ltMzN7BGg33M\nhoMmjSJ2LrgFeVFajo6dQaYDxemRYxDWBxMGO85a1Ft7jHour2RyNPb5GKN+l17Xfb5+7Rq15Ik8\n+sg+o5571iioyafu/rP5hOGuWygujT7MgTzrcXUdJcw+jcZKKLUOLJUEzmtTb/DmVojjlERy14aj\nLXrC9JhNzlxoFWfINAaO1tq7g9fDcO+Gn5hKrfKb9O1rVPAes7f5rYbhubrnuvF1F3lhaQixMFtC\nBcIKU0RI9Jfp6YBcNk3dDQgbVhWWSmh7k+oWncjlMxilULKA5/MpEe59bSnL9aUbp3sXRwy33ffd\nbkVPrI5K5RA4YyqKKxYSds6WoCWMGKghWba5232ZKT8eQKGag5ZeMZkUFYFsUtpovu+TfxCAR/cf\n5HTs2rxz8XFMLkw9Kj73lc8D8MI3v0wpB54SRZC4Z+klEZX8VlUbsmajVIZIjDJrDKX8blVMCQL3\nvMt80aQm3RFlpX3umrI1pRxOlvkSJL+tk2q2Jd/j4Mxy4/h+AIr8PuxM8q2iDloM2W60RDfGS7Zg\na+gObuVsSiZju1hkGHkneVX7cGGY5gQyRqw1PjcujvVaTpHCbLrYABhohvyyyPnaiy/6Z1eyJhpd\n88hjD7v2lJprt9y6dnTrBN30DwrT5BzdJWPv3QzaO//BXd0ereztdoqMC6Xx+aDOrPRW0G1/7w91\ndQ0STrWmWAtxBp6RpzFY1aTF3B7ja9iCyt5+KLvtd71dZdmUuQyQFzl5LnsCAZXsf1VdMJL8u7LM\nSBIZk3GIlbmlNZycHvl7LSfuIBeXhv2hY/wFdUXqpihRYegYGTRFSDlz9zlbZlSF+7xcLr1xmiQR\n3Z7YGYnLMwYwtkDfRcJ0G+Zr0aJFixYtWrS4B/z+JaCDt5wLU/Pks46B8V/+1/8Vv/HrnwHgV3/1\nV0GSUtNAsRg7l7a2MXNhxfz2v/gNLr/mWAI/+pM/zqd/9EcA2Ll4nlyMysqalVtgLUd9/YD074rn\nURYFoZzy4jimI8f86XRBURT+d2s52b115ZB55p5rXKZYsZZrk9HruWRRW1vGkrh9/eabhGEij6KJ\nYnG9hwkL8SIURcb2tvMGdbpdDm45q77b7ZLnjet0Slf0n+Ik5Ja4z++E3Z1drl13CYAm7DDccaGF\nreGAiTAlTg9PGOeuvXqy4PyF+9zvpwPGExda0HsjBkPn8fnwE496zZ1zWwNU7Y4YxXKBllN9nefM\n5XBybnSRxy45T+b0yhl5KYSFNS+PBf/ODavw7ybo9QcksWhwVZaRJPOPRtvMJayiVODDT0EQEIkH\nNc/zb3uKVeD7ezQaMRGPW3MKaq7Ra6fnb3ufte/WrwmC4NuGAt8LKs1Y1O59JTYmkXBeYAPvMg61\nZV64d9FZVNw3cGQAYzUzYcMt6hoTu3EXZYZIu3dak9O1bgw+bIaEI5cQGm7BVtd5SoLkhFC8ilVZ\nUyjnUZicjpmeuXG6XFp2d1zfHh8ccnJ0svEzGiyVvCOtFUkkyby69i5qgyGWJPvHLj3FRx96DoBR\nueTjH/4DADz67PcxnbgxP5sekcZufr9z4xpXz9z3qdarVAJAN+w8G9MTZl+qgibmBcoSi+e8yEsq\nGfPW5BTZZif+s+mS3pbr40SXaPFMzadL4o4QU4KIS/tu/F4/OOPVl5wXvKPGXNp243F3W3N26saj\nqk/Zv+jWlO4gopaTfLksKMRlFsUBRXPCz0tC8cQbBUrVco0ikPFoKqgl3SGMtA8pbwJtnD4eQBQF\nfOjJjwCQncwZ33jb3TMu6fTcmOrqmMGWG6dBCJNTlwZx8dL9DXn8PfFuAse3Y9Nqre/opXq3ftOd\noNd5ctaubVX2Nk3E95rdPgxeVRSio1QUS7SsYS7834QLzSp0yIoB7DSqGi/eu57nNv57M2/ubs+0\n1vq9JwgjOh1JHKckFxa41lBJhOL4eEJHmPBn4xN6PXd9GIbct+Pe7/26x15Hxr+CVBjeR29eJRQv\n2F4cEOy5axbzI4x8X5YVkYxbVE1Vu9+NkoThlttfd3dHBHfhmfp9NaZs454MNKVMsJ1L+/ziL/1p\nAJ7+1McpRaDr9OY1/vk//gcA3LpyRFfYVkFheedVJ5/wv/9Pf5kvfuGLAPzCf/iLPPupjwNQKk0p\nIUKjNUZ/ay7VezKh7tLKquraLzRxHKyFfawP0dRVTSRt+PSnP8J1idd+4etn6NRdM5sVBFqYVFXN\nSEJiwcUdoboCNiJJ3Sa4LGpmYm3YuvJtOD46IpcQRb/fpxI3cBiGJE0fBoqi2IzN9+KLLxJ23eAc\n7vY4t+/CBoPBiN62hGCiA6Yz9956/RGHt5xh1e8tePjhh6QtXWphInaiDv2LjsF3cP0dz35Stqab\nujaq4YCuGKaxCXxYahBts8zcbxGssV/eZYzY9wm/vRuK0OfmVLVFy2Y72tri5g0J06yNl6qqiOOm\nL4NvYdyBYzztC0vxIx96kqNTZxR85cWvubBM00657/st2mqd4bMmq6Duwpha5nMaXnFVL33eRV0r\nCol9F+GCnVI23FsR3cr1c1EoEiWL2CAgu+D6ZFyVZIlICIQ9qrlboPZsTNJz7/119RpWRFC7b3dJ\nCnf/w90ZJ8rd/+SWRUtIvCwNV2Rch9WQWzePN37GQV9j1nLKIt2E+azf0KsCHt1/HIA/+RN/isU1\nd//Dt14jXTjD40bap5Znn04mdEWQ9tH7HuXamRvbhsqzMrXSWFm0kyBhIKH+CO1DYlGUkoZuszPd\n2Ifrsjyjv2Gc7+B4zLl910+dTgjC1K20wdpGHLlm/5w7lPXTgBe+7FIoOsHjnPtO99xqq8dbr7sx\neHzrkO/8Lmd8fWi7QynrQtrp+g3HWOtFT1UQUspvORag+5x0tDeUtVVe+NjUiiy7g1WzBs2Kbal1\nwCMPOwmEF37n97h14ELun/rU0y4VArh162ZDmma5HPO7n/0XAPzAD/8EO+cuSBvMKuTHWgj9tlD5\nmlAuq0NaXdf++3dLmTRw//luQpkW6w1Mi9LN+rcWYluTMVC3k4H9/6ZpjJFkXNdnEi5kFZKzKAIf\n8ltPMVA+pcWq9xh/atUGbVlJz2yAQGu/3qNKtCSK5sWSZebGVac78MKbxDXjpZtz3dwSWre+PvXY\nozw8dPlQXD/EnLpDuIoccw9AnRyBiMoqmzA/loeMSwayTudljRbjMY4i5JxFvpgSSF910/iuwrtt\nmK9FixYtWrRo0eIe8PvqmVqlJCqUHI1rYxFZJJ597hnHdABCa3n0cXeS+qt/8S9z9Q3n4lVJ7Nlq\nRW14/nf/DQBX3nyLH/4xx+T4wz/xY9z38IMAVCjP8gq0epeFf++w1nq3a1WvXJtxHKNFbCzMLVbK\nunzi2S6PPew8Fi+/MacQEznLQ0JJyL1w7iK9wFnsJ2cllZStsKTUog8znh74JG6ttXs2YD6fU0sC\n+mQy9m3TWtMRBmUQKM6d293o+SaTCedH7tqyKhmPXXigMopIwpujrW3OX3RJ6mVRcvl15zl88JP7\nbA3cb85nq7YsJ2cMheXX7ySUIkS6uzUklnHRTzTFTDSGsgATSnJrnXiBvPWyK7eXaXlXgucdYFBE\nwiKNwojlUphRSYe027Cbpt6db42hFFaJI8iskl4bb0UcBOxLIvCTD+5zs+PGwpXLCbfyhh22YiOC\nQq+dUFl9fZu21EogVr2foui3KKD9MwAAIABJREFUYJ4t6QmrLq8nVHIaS+qYhbDwtqc9tq5IKLPo\ncCTsz6sHh2xvuzEwPI0Znsi72EuZ7bjxe2xKapnJ0+SMUeXGwznTpxKNp62jIbWIKs4HBZOFe97T\n0xl98ZoWecUkc6fPYRCjq3jjZ+yEqV9XwHlIAGpTkMTOs7Z38Tw//PFPA9A1XS6/9QIA0XzJO++4\nckc3p1O6Mh5UEDKt3Rj+2Ic/xsvX3dg+mh37/ldKEYuHWYVwVrlxGyhFQ2QLrEWLl1hjaIprVWFB\npTfzTFk0CyE+5Dbw6xo6pBRdrG4SMRw4puDOdpfp2IUlJ2XKzQPnsdraOc907sbjtWszpjM5+ese\ntRUPWxquEtyzgiR19yxNxXzqxktYxoSp6+MkjlC1CHXmNWVDHFAxR4fZRs8HYGztRU+pSraE1bW/\nk/Lbb7tkdKsXmNL91qX7znP+vPN2js+OuHrZeYD/4d8/4cNPfwyAZz/2SfoD9+ymqpiKl/jWzZuc\nnrgw/mQy8aLJnW6fRx59DICHH3mIUIRP67paZ5WsNhG1rl12Z1hj/T4H+M+3ebhZ93XdPtcDCW8l\naYQpmjBrQOONcqkSqvkxzzTU696utVCjUdwWXlxtkmbtZ29v8yYIhWySJIFP/j6bLDE4D21lS3Tg\n7pl0uxQTN/YubV/gwQsuArITDbGnEi4sCrR4smbVkuXEeSq7lEyFIJOXJWPRllqe7xE2otHdFIke\nU2QV/Y57p920Q7ZsyiNN7yqi8YEaU+/V9dqu/uN66KIoS0IJjZna8I3X3gTg+s1bLMQFqOOQJnHf\n1IZQVqvDqzf45b/6NwH44u98np/+kz8HwPf/2B/yquRFZVZsPn13m9F7wWC9iGikQh/+SZMuw0Fj\nhGgOr7ln0WbBsCcCkYOAUMJ22pRYcf0P0vNcvN897+J8vQpTmiGnosx8PJlgjiWnQWtiGTSqtCQy\nQPP5jEji6CjL5GwsrY64/8H7N3q+Rx55BBOKqrtSWMmFmJweUsvgTNMeqbD2xidnJCLqZ+uCt958\nFYDlbOqZmt1ez7exKAqM5MllkWYurI9IleRjR38+vlmghu757Fblx44xq9pU64ZV8+9NkXRSr+Ye\nBsbXgzNG0x86w7daZqhaxCrRjW4eYRBR1ytWjKxzdNKESFzbBzfeJlu6+w86CSdTYcmt1RA0BiJh\nKznDeMXAWUXz1G1hPnsXuRpVmZMlkj9gZt6w3dIBSeDCIR/uPs31U1dP68rkHSayyByenVJKrl4Y\n7bIj0gi7B4rOTPKttiOmI2d8XR8ecsE4heGPzu/nWEJ1dRFxvCs5ErMzTkU1vy4UlRwkFrM5gVDy\nZ+MZZrE5m68XD9ZCb6u+Kk3JpR23Of7QJz/Nduzm3De++lVOT13bUmN459jNj5tfu8qW5Ludv7hH\npd0ceub7/iAffdjl8Lzw6r9x1RiAWEd+Lalx9cfA5YoUsvhndYXSDVs3QPRQKa2lVps943CwTSFj\ncFZaOtJn5XyGRL1Id4YUlWtXrxuhQvdOTk6Pef4rXwWgv/Wd7F107+fgxlVmmXuf40VMnAg7r5wj\nwu8EoUWJejoqAxpB4cAfAPv9LsWyYStWjUIMi3nFlctnGz0fwJWrr3D/g48CYIwiktSHTjdhPndG\n6pe++DwXz7swcjYbE4fOcM/yMVPJ4/z6177Jb/6L3wLguU9+Bx/56DOuPdMZb8lh7/jwJru7LsRZ\nG8P1Gy6EWxlLVyQinn72o/zAD/8gAA889NBq/rHOegO/GDK480Nac9ves5L3WbvfWt4Ta+zCJisU\nHAMub0r8WbMKUZnq9nSAJiC1tuWZNaYexr5rL2xq/9XvCn1uvt64FBfXhtls7tnS/f6QydSNn/l8\nSncoeV5Ws5u4vrvvwYdRkjM1rkBJ2HxmLD2RGzqdTRjJPYt5RS3q6Z/+wR/huuwnf/fLn2dL5Ei2\nuinTMzc2ojD0FUnStIOVNTgMw7t6xjbM16JFixYtWrRocQ/4QD1TNSvrXVt8raR1poJWKz0QHYZk\nE3f6+NW/9w/4R3/fJaAvJxPv+s2LwjN2rLFemwm7uufLL36T/+W/dbpUz//eF/mJn/tjADz1iee8\niImp61VZjzWH6t3KvBlW2lVWBT4pOElSn/RalIZK3NWzZcqNW+6ktshrEmH1aFND4I4ZlQ3pjpyV\nfuPKm1Ty1i4OzxMIq6qqSoychvK6IhJX98UHHuHqtbfcPfUq7JSmqWegFWXGjZs3N3q+osgZSZhv\nuiz9PbK8Yjp2J3lTlmRCHLh565Bzu86lfvXKW5TCDut3UsbSH93eklwqz08mx8waMdEgYCmlBh59\n4DzbaeManvL2Zdfe8x++D3VO9GZUhhJtKWvC1UlR2bvKB33skQf5+le/DMDhjYxUTkI6TBkIa6js\n7zCbuJBAHCf8ge9w1cW3tnv8xmf+FQB5XvsaiSQxZ+JwqG6dsli4/jmYLCBypyhtDEZKsDgv27qG\n1Co5dP2UuZ5wb+9itEacJ8vcyTsjxIiHI+guuD9x7MsLo4/xmnaEjuvH1wljN6YG3Q6x1JPUtsBK\neHea1yAsvK2jgO2HXF9ljxsq0VFKrpzRODVubi+4ot27NoEiCJrQ1IClnFYPbxz45NCh2r0rocBh\n2icWr6hGUYlfv8Iw7DX6SQveOLgKwBuvv87JLcdqvbjVJxGhXKMT6p4LfU2ygitXHXv44Y8+w8ef\nfBaAK4cvkzd9gkI1Ce618R6rIIgp5WHKuvLrXL3maQh06MR+N0Celb72XB4naEm6zRcV6ZaMqbDD\nQuqgpQmknUbDqODWofP0vnr5Kp945rsBOHfhYY5Ef+6br8947BEJd9s5kqWApSaTNIW0E7ETiTed\ncI3IUPrk/yCOCELX36enC956c3NGZrXICEX3yBISaud92N3dZ3vkvEg3xm+zv+c+G1vy5S+5UO39\nD17gULyd05MzTsUT8Vv//Nd4/vNfcM8Vhih5b6GqWAqh4PyFiwylDNbNWweYwv3t537zBi++6Lzr\nv/Cn/gM+/knH/rTW+Fp4SkGtN99aleV2trGEwKxa7ZGoNUY6t3vBmpCitcaPnbouCGg0vxRIKgxK\nrcqvrQcOFZ5pqsyauqJdhY2srf3vWmu5o0jjGowxnjmY5xXn9tx8KoojlBBSNNYTpOpaI4R3Ll+9\nylIG3/j6EedlvIVRyHkRVx70t0DIEjlzhve5kkL3PfwoZ7cck3heWVLxOvV15PutrCzTqduXovAC\nRlIMwijyc3QT/D6H+dYol2r1TSozcnw65W/9b38dgM/8k3/qaxApq1fuyVphm7CKsVgxKKw13kUX\nKsVMNvpf/8e/yvPP/1sAfvJP/Cw/9bPOsNo7t0cu15fGrJSov2273xtaBzRqtnVVUUqIKAxjaOoR\naQ1ihHzllSM++1kX+79xpghFdrQ/jOlL8d+aJUnPLY5GKRZSrFgFS/oisDjqhwz77p6TeUZWOcp9\nrWoCCe1VyzlR111TVRVh2IhjasqmgN8doGzNtXdcvlqtY/rbO9IW7WV/j44OWIpxdHp6SBQ+DEAc\npUSSB7Q4O2VeNMbRgsO6qc90RiY5Ycus4uTUPcdscsyHH3CU2HmpePwjbvHv7G1zQ31D+maMsfIc\nNvH5R1ZVd8V0293us7fjjNfXXrnGUsZCp9MnEEMy6XZYSJ/94A9+P3/+z/4Z1ydmyUzyyP7NF77s\nF67ZMuOqLOxpoL0y/iOPf4itc0708vjslCtXXPh3fHLiVduDYDWmjFrVGVyXQtBa39UzEvaoK1Ha\n1jFbYlyga5ZjF+p66+RNxlITsqjxtfz6CRRLEVOdKT9XSqtXRutijnrRPePopE/+IQlB94ecSo7N\nQXqF44nrE6VDqoZ6r2uWYownuk9PwuDV0hL0N1fPTuh62QOFIhDavqpLFtKGm4e3QAqEnxzf9PmX\nw50Bw74L6Z5ksPWQCyM9tGX55kuuwO7rL7/Mc9/3KQD2z13ibOGeJVTaF6XNsiVVk0ulFcZK0fFa\nUzfrhDG+BqJWMVW9KrL9fpjPcnTkOnxyPKeUA2ZUaoLALQynkyULUYxOIsvWwF1zoAuS1OWNXb16\njU7kDIQ8q9jdduPxxuEpFrfWPLqfEsruplDkMi66aUAiG52qA7T0cVZkGMm/qG3EQtbBG4czDkSG\nYRPY2THzI8d2zqvKF7ntphPuu+juf3ZzwYU9tz6+9MprvPz6ywBcfvMtrJH3b2qCpsqDMv7AFgch\ncZOSRUV+6J738OiI8xdcPwxHIw6ldtvu3jneefsyAL/yd/42o9GfA+Cxxx+mFOV4HSqqsmHobt35\nGW3t97ZAKW/kWGv9Z6UVhjVrrRlTdYmRddwqhZUct7ouoBFQVeFKvNSoVRKmMitjzcKK8bdu2K3l\ngqG8d8GFNe/GmLKUIt0zHGx5BfTlsvA5XIGt6SZdab/xrOJQgZb9cncwgpn72+vFjJ09tyeoXkB1\n2kgPBVy65Obr53/rszz/hjv8ZLYgk/SXXif2uWYYy1LmyMnJCaORWwvdutuG+Vq0aNGiRYsWLT4Q\n/L7V5nNfrFnFdvXV1avOLffL/+vf4rO/8S/dJVXlpd1NbXwV7FCF3hVXFQVWGG3rCcjWghJ3fxRF\nHElF6V/+y3+Vl19wSZg/+kd/kk9893cA0N/boWoaVK8l8m1giK/XaUvTDjvbzv2sVchw6LwaN24d\nYxomWxWhJcwzGkYshLHW6Y+I06F/3vNSHfuJS49y88yFImIdEe25a55+7jk6I+cleuWlN1xeKDC+\ncYiSU1ISBkSNgGC6Vv/LWupqs9CC1oqTE+e52D5/yX+fZ5lnEMahJpcT6s7OCN0IkVYFp3LCO715\nhXkm5QXCmED0dy5e2qWUU1q+zLi4655pkHQ5uO5+d2ZSHv+ES0o99+jjFGfu3V49zijMO65BauHL\nI7jTxebnhnfefoOGQBRo47VbysIwm4v3Es1wx7mbn3n2QzzxqDvF1tmSP/4TP+7aPy145U3nacrr\nkvHUvdsqCRmKbthzzzzFRz/2CQBefv0yXfn+G1/7um9xGIaeVeJYMMJGDQKfcO/+b/NT1GR6jJF3\nNOhvEUky9Gyx5Hgh2kmHX6eU+mq9bsrZkQiMqoRY2C9lVXkNoaOjAwrRh+r1QnJJ5s7HI85dFE/G\n/oPYyvXJIltA4f54OakgEO9YZOnJeLeZYijhnKk6Je1s/h53hg97bbKqLNcEcWuChikZ9phm7rmG\n/R6P3u9CnNaUXLnlBB/pXCIXb+likfHQQ+5dn54cUUqS9Xb/IrWEmKNAUQsxJEojTFM2pq68t7Eo\nCyoZ56UxDbEIrTVxtFltPoymklIZy2zBXDwOF3e6BIHzRk/mYx8aTRPNQEJXxfzMCbQCx8sZndB5\nYi9dGLL/gEvOvnb0Ni+97NjRfR3zxMOiLZUoX94KFFXZxK8rrMRwdax9bbjFouLaoVuD3rp+5NeG\nTRBxzdfqDKOKWhiF+zsBTz7u1tN+8iwPPeK8iJ/5l79GLYy2CnwaQhAF/v1XdQYS1s6N9aHJQGui\nsKk/CG9fdevs9s4uqXhMbt685cNwVy6/wOf+5f/p/rb4BHu7wkjud7FZQ+554o7PWNcVqgnVGYUN\n1jxQPqymvK6TSyEXPa9IU8kasLSWyJMgQqxs74pwVUIGs1ZazazHhm6rm7vCmv9pLaxnLXeVOqHU\nqozQeDxjLKSFRVbSjPZOFBHLNfOqoJQfSKwhG7s5+sBgh+NTt3/HYeUJO/O8JpY+DIOQXdkLb3z9\nZQayJqmsYlZLxKbM2Opvy/WKIhfRWuWYnM1DNuNnE3zg0ggrFoJ61/fu/6si5//+By436jO/9s9J\nJTSWRiG1DCCrlKdlWmuxQbPB1RRVkzOzNlCURovxEkXxyt1b1Hzhtz8HwFdf+Aof/y7nsv+jP/cz\nfMf3ujBSFIdkMoE32Y9NXfvcFaWUV0PXOkQLE2V8OvY13qzRPPiAWxQms4o333GTfGvrIv2hKxw8\nz7u8el1E9Y6HnGWSbzXtYmI3ybOqR3/krn/quU+icN+nach4eg2A5eQdbt5wn/fvu8TenluAjo4O\nfKz6Tnj77SucSeHi+x95gqqU/q6MV3Q2uiYVddnawFRqnE3GcxYi5lnnC8bHLmdjPl+QxI2sQo+4\nK1TuXkzc5BwVJdfecZt859yDfOl5FxodXV+wJxtgt3waiT5gwhOUdX2m7NIrM2+Cr33pBZqlxdGH\nxXDIK6xyP5Ab2L3Psd6sqfziHNtVmGy7H/OJp58E4ObREaOBMxB+5qf/CA8+4NqMCiglTH10fMLj\njzuW2enJMYc3Xf8orT2DL1SrueJyppr8Ke2U/jfEvMqaPYFR3KHUsviXAYvAva8oDKjF6NjZ2aaS\n65e18gebxWKBkb8dzxdUtsllNERN/kaQoUSw7+AoZ2ylSHWdoGQsmTxES6HgJFV0pSZcNi8hamps\nWu/u3wQPP/gxz4hUWJ/TUpvK50t0uh1CCQXtJhFbIlkxmS1Jd1wbHnviI5zdeAsAW0z4nu9xxu8r\nb91iKUaTMimZGDYmVNiiMXhjFI2ApkY10iS1JpGxnSYBSzlMlFXtGXF3QlUZaqkEUS1Lv9cNHxgR\nCeOWak4thuCg1+W+c24D6cUZmTCiC2uZT6Vgcj/m/MVHXD/FPa69+TUAXvrmdc7vujE7GIb0pV5l\nSblKuahKJxcAYANqGS8nkzmX33Fz4ni+YOfSnUNfDaKgoCs1JMMIuqJ4ff3qGEo3Kb73e36AF174\nCuCEOkd9Z9R00p5nL+emXInjmtJPImsNedmsDXptvdY+jF4dHrAjh7pAK5ZZk2O1IB87g2sr/jBb\nEnI1ywl2eRfyD8agm1qOxlDLRKvt7fmRzQbUjQLvNAijHqkwaxfGcnTg+lmVNTuXnJSQDQxW1j+7\nJsOh1o2jtfa8mwntM4nt7XINd5P+si4ZtJgvfYittpodyQeOyiVVLjlTxnBRHBHf89QzvPy6C632\nopBI5mWvttRnTtC3HvWJJNcp7qa+35bjM6yw+QKgEENbBYpCBIYtlgsX3W9t74z84SCOW9HOFi1a\ntGjRokWLDwy/v7X51uDLaAQBP/bjPwZAUlR84V/9DgDz01O6SSOcpyjFeizL0p/OZ/OaIO74e5qm\nDIHS1GVTTXsVIrQKkqZsyGTOv/7MbwPwyte/wQ/8oR8C4A//1E/yyIeche9jP+/7IPgK3cvFlBsH\nzvrd2drxdX72zicsnYOI5fyUTsedjG+dGh564jsBePyjz7Et5Q+KZYdXDt5y7VRD0i2XXKdI6Mbu\nbw/fvM7xTaljuH2JgYQiOoP72BWWTzF+nte/7vozL+ecHLmQ2HI58S7hO2GxWBBJbcDZbE6cuFOC\nRlELUw9bsCchsMlkwa1r7vQ2nS65eNG1qypCxnKqiEKn7QSwLAyIzs5oNGR86EIt86Jkb9dVCL/0\nyCNcl1p+i1vXOFvICTKISGMXeqzDPiZ0p22rD1HhZuVyAMosv50lVzcZ3wEYESIsCrJFw0LR3tNa\nLOdE2rX/wfu2ee6T7n1+9NmPsTV0fbJ/cY9SvDbHZxPeeMOxyXZ3tglFSPP05Ji5uJtNVfsxq8GP\nd7vGtAk0vgTOJqjqjFQ0cPLlkrp03qUkSUmlTl/8QMpuz43B+uqCTEQML/UH9OUUfuPmqnRQVlRk\n4pkYDSIf9q2CkIMjl6x/xbxK+IR7p+fS84TanSajmWUude5MlpLJKXb3/D75QkpGJDFGbRgCA+Ko\nTxg0TDrtE2tVlXvPdhB0uP8BFzLWZ7EvebH92P082HUeFB0lnNsSxtr0JtvbItwqJXUAOvGACGF6\nZnPChkUYxpimhFOgSOSk248tEwkFlUVBIuGlJIoJ4808U7Os8PdWgSVqtNq0oZQE37iKmYlHxvRr\nzl10/TfYPsfOwHlBCUsmJ1IPM9j2GmjDoOKZJ13qw1e+/GW+ccWtfx9/+oA9edZxnlHKepemIUpq\nEtYo6sp9PjlZckPIF1FXs3thc+HVMOmgZIwssyWxZItffvM6GPf9269f5d/+a0cqGna7jEbOs93v\ndXxfHk8MmdQ8dPXpmpJM1pMOjDWU5crf0hB0rK05PnLe1LTTpWq07xLLUx9xNUIfffxxn3ReVgad\nbqAvtYbG47OcLyhlDqlg5R0LgoC6aARUNam4lQ+vn7AnifK9OOSrv/d7rs3LMT/+878EQOfcAz4E\nbdQqbKeMut0Dtc4WXIv8rHumbhNCvqsntD5MaY2iLzUle9sjnrnk1vVoPvb1HM+dP09nLmSi6yfs\ny9qW92KGXedFil++xlxIEZkt0OfcmNy5eI6DsdtbFtkE0aymCi1FKSWOcpgLy3W0FbMltWyrKmdb\nav/Vde3XvE3wgRpTmpXUwG0hV7VG6taKx8V4ufTnfom333wLgC9fvc6O5Ps4gkHDjFvVhgoC7anQ\nSikv9ldXtafpWmNQosRqlfWMryAKfEhpcnDEr/7yrwDwtS/9a/7En/3zADwluTDvB2uND8n0hx16\nwsKrzNwzu+67f5ueGEQ3jo/Zu+iE/574+Cfob7uCz/H2Ng8/6SbJ5OY7LJZuMo/O7Xp3+2J+5lgb\nwLls5/9l782CJEuuK7Hj7m+NPXLPrLWreu9GN7qxkg0CJEGAJAbgMsSQRtmIlMlk0nzJTDJ9aL6l\nH33JNDKTmUwjs9FiGg5nxobkCBgS1BAg9m4AvRBo9Fp7VVZlZWbsEW/z566Pe59HNgiyoqYkYD7e\n+enorBcvnvvz5fq995yLySFtavtX38JEUF5K0HsK611y22+uP4b3f5yMjatvfANX3yR6cKAMomi1\nENHm5iYSFjgbDAZY4yK0nhLQPNnbrQi9LhezFQoTnjgb/XX4LKw2mBls7lI7xoMjdFnhtttZh+JQ\nrTLAepsWpW67jTPnadMbTedI2Lic3rmEW2zDaSGhmrRZhJ0YwSYL/G22EDZXywkD3uvip1At51FI\nhZIXMaUEpjOOvxu4wshr25v4/G//FgCSwAi5oGcUN6E5uajQGuMZbaST8cTl0fT7PWzu0sIyHRzj\n7dcplFnALouT/kjNQSefoNR9uaTzhULANJp5PsGUDZb1/iZCroeYexpbe6x0rbsYH5LRF3oW21v0\nnIfTBeyc681FIVJmcQoboNenMPJU57iVEwNUXvAQ7dAY6HnriJrUb61BikHMDJ+0gGDhyLAVuHBR\nM2pgNlmdCaY8zx1gynIZ2pFKuhBnu7OBtsfCl9Pr6G9TpYTW7qNYVAZJ4CPaorHq5ecwukVssV6n\nj5QPbxfO97C9eR4AcDy4ixGHekkLkft5NsMmF/cuyxR5TuuNMhlUFc6RYmUCkTYWMqj0Cko0OvSu\nBospplN6nxv9LnzeiApdouS0CY0Qsxmtm/21EBGHN1u9HfQ6LDOQ5Og+9Bg9r9jB3TGtFwbH8Jip\nZwsLyQqhKgDYzkCSaIxG1Ml39qcoObdlbb2Ffnf1gEiBDJmhCZ7pFDmrledl6UJv3//eD9GJOU0g\nDqBYdqbd8F3fN0MPc/67zkpIzhE0xkLws0khl/uTXdakNgZu7s7nY2Q5zfuNjR7WNml+T5LhUtIl\niJFyaGkVJEmCnCVgskUCr6pHapf1OYuigGTZnLWNDhSHbq/duAlZ0RGLAr0mfe4GQE/RNRq5U683\nKnAHP2GW+ZfA0rCi/KZKbuG9xlQFa+19yQbYE7lXnu8hbtC8SdMEZ3bowDa/OscTTz4FAGh3u/jh\nn1G+9OHb13EoOWUg2MFzT7Fz48q7sIbW+0RIGHYa9M+cwpUZjX+5teZqEWbjoUsTEJDu/QaeQoeF\nvFtxCLDjRZw4rK6COsxXo0aNGjVq1KjxAPiJeqYU3qMsdcIztUwol1I64c0//ld/gtde+T49qBdi\nznowJwUNjTVOv6Lb6To38Gw2R8CJq9ZTsOx10lovRctM6ZI2DaxzmZelwSMXSPTr4x/9AE6fXrL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6hSWDtNhZIPt1fulvjeDyj02e800WO2I5IJttg47ex1ccBzYmoMLjxE6Qnf+/aLGI5onHid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\nAIDW0TF6vPnnV8/glTOvAwDSLMKHDz7kefBQ9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bSZkOM3Y4PosB+hzJRyRdqvYQOE6Uch\n4PBha7SyPHsehQg5g9PNDQTHbxhtYIrU8KgPyanwWdmDY9+bgOSgOY3EGiZKuTCcRh+ORlD8Hsr1\nJrJ88o0fBAHaRVydMRj/5fhCznRu6WLPG1/MvV7X0p312tRY0VjgKaVze6CZp59r7AGlxhS049g1\nFUXaivQlyQhlK9RZgvcFqAXXc/Gtb/8GAOBr3/w2Kg0yWFzXQcQCiwCgvCLzJ4d0C/rHR8CZTros\nEdTo8qo1E8wvnwcAXHn5NTx+QLEKTx7exz47SJ2jFsYjVNg/5FToJMG5i3QxtQ662N+kGBjpOZid\noT7sd1q4/6Q38Rgdx7HUXp7nT+3jIjNT63EMxkkxTyEEZQpzW1ykPfc7v/M7Nq7qzp17NjaqVCqh\nzKEK1UrFnj21Ws3GLMZxBM37++rVl7CyQrGPN258js/vkPGYZBrXP7870fhibeC49B6ma3VsPqYz\nZW19By9cJANuc7NlVeY9NQsOBYXfGEL61MfjeIBhQnPTaR1glNIcLy6/Ao+fH48GyPisSVMPU2yI\nz8xU0We5FSUNfJdjyNwyyqC5EdAAO0VJliHk7LxJmjdKcWWJ6cvVJSvPcVR24VU5W9T4aDOV8/zS\nCn7rV4jaO7e4iOExnTEri5dx4b9+AwBwcOcnuNOkEArVKWO6CI/Yb+H6Ns2hN1PF2TO0HhuNBhRn\n0h1vbmNrnbICRbsFzcFFb7/9DiRTbPXmFF55Z3LKXZvcggxpFkOb4s4TNg7P9cZGlnKkdeo8x4Xg\ns1BB4+UXrgAAAkege0xGRJpLSKdQRh8LfkJSFh8ATFcDXHuVst62t1sY9mk9hpmxNKIUGMsESQmo\nye9FnSWIc5Yb0gl0nU+B1QBgUVkMNMQBz8NGhNijdRLMzmKb6ey93R184216j1rneLTOsVRRAsnv\noh7UsBSS87MynMMx7/t8mKG6TGEI04tTyDNe25VD1Jt0T4eHQ0iO9WvMllBpTi6EfErznbbTdtpO\n22k7bafttP0S7UtFpkqlEkKGnCEMKmwte6uXEDXJ07migZBrRh22WqjXyWKUMJAsF5/nBoZpoTQO\nrafoeT5yG3iuIRntyI225S+E4yDj7KM8S0+ILY71cgCMg9RNNnF5BwA4PjqA4WDkOI3gTxHqZGSG\nTkxmdwKJMCPP+/2P/wgrM+epP4mH73zn3/GTcqtRNLtwCc0l+szVr/w2ggLtyAy6GWdyuFVon36f\nREkRr4xch3Ad8j4aCy40w5kCBtU610HS2mrkPLMJ2MjAACPIPsGmMBfGQZEQNqDcSUOomNzhfqIx\n6HBQeBxSCRoAvlTQ/M6VBnLWAhnmKfSQPi+dNuocDK3KVZuAkA2H42D0sgdRaAZ1d+AWdcggvlAS\nAYnFMi1lYLNIXXccCDnsDSzSpLW2orPnzs2hZ0UdcziqCCI31nV5SjTyqXqDTylvWepTKXVC8HNM\nO0/PNPDb/+pfAwCiKITvTe5FVao1rKyep2f6wThBQ0cWVYnjGClnkTqOg6KuphQZpKDPh1mOJC76\n7UJ6RF/PLJ9DY56ydF66+o7Nttrd3sbWOtEtn13/yGaXRckQvZDmcGVmDo0p0l2K0wx9Fl58sPUE\nSTJ5aSet9VMaXlYQVUr7MyHVY5TKhhIYjYDFaT23ZN/XSy+9hDfeIM/4zp179pwAgKkpoqCUVLYE\nThAEcDhDTCiBlNeV40icWyFapVEpYzikc/Hxkw3EE2oUpfHA8sHT003UqvSuOt0h4NC589Ibb6Hb\nITp/GB6jN6D95HSBClOEsgSMNO3R1Emw16X3sLXfxvI8hSnEYYIkofcwGB4hYQSq3PCtWGw6SsHy\nPtBG2AQjiRQxZ7pBZdAmmmh8APDCV16BU6K1/zd//Tfo9fks6XYxXafzYNTPoQb0fv6nX/2X+JVX\nCJ0J2200eN9UGyXUlwitKHen4BzSvTJfaSILad88X53Gf/Pf/zYA4B/2H+JvbxJ6dbCziYwTp3RN\n4NzL5wEAtXIFt+7eAgDcfPApfKal3vjGi3jh+YsTj5EOsGLdjevQCUibkJKlORTPg1TSovSXL1/A\n7iahaa5J8cZVWptlT+H8eUInW/0Q7S69Ow1AFtS31lCcVHDh7EXMTdOcxKMYtYC+q5PEyFlnClog\nL8JrDICfI5j8T45wKgXmGF2fyuFdJhTXcYAGJyekjyTyIX3GnxaQXVrPJozxcGuNxnvpEi6dI4bl\nww8/wICzmbM0sfV6S8pB3SnzfGaocVaue2BQ3SfaTjkJwGK2829eQpHK2Ds6AGtiY/XiFZTrzy4j\nU7Qv1ZhyHMdSRL6vUOEsmmq9glFIm8SYMax4cHCAGYaTw3AI9FnwsxQgs6n8jqVGkmRceDTLAJ+N\nNXUirsoYA8HZbWkcWxpA69ySLdQFfk6e/4J8sX/cyl6AEYvVmTyB9shw88oGmShiSHwsTXGWEVyA\nLyzXz9BqUcyJTlwsr9DGqM4sYsS9aIcpBoZV1bsR1tfpkhoMh1bccG7GhdB0IEqtkYbUnwuXz9uC\nplFyBMMXvTQG/qSQrTEnaplJSK/g+sVT8gVZSGMaHmwhZ4kC4ZXhsGyE25yFU2KxNs8DoiH3N4Uu\n6ugZIOWVnY1GCDkNNm8dolQoYesUXvGzgZUuyOMR3HKNu2xOGHrPblk6Tg8fDoc45NioIAhsPNFx\nt4chCzMO3RAui+Wtrs6O15Qxtj/6Z1IcrTGVa2g+lJ6m/+RTGahjY1Dax3i+gufTXHleCQKTC5Pm\nWkKyCGOeJrYyQRD4luLWgDVGtDY27swIhQFnoIZRYqlGCQHJWTRpnECZIuOrgTNc3PjMxefw4pv0\nrs9dvoKP3/sHAMDhzhbcIj4uA445Yy7LDdyC0dfKKhtP0qIosjSrlMrOIdU6ZEPe86yBfNL4ovip\nsfFVvIt2p43tbYqNWVlZsY6cUooEgUHVHQoDTZzQ+tcaViHcZBkyXie1oITf/GeUpfjRxzXcfbA+\n0fiUMsiLOnqNOs6fZcmMOEaLwwKazWk4ZXqfi7Or2NqivdjresUxC1FP4XhsuJcMhpxle2t3G92M\nr4jYIBsyrTaKoFI6X7xKFYWeoiMDOCVOzXcVqlN0vlecEjRLtTiBhutN/g5v3focQ856DEeRjUGs\nLUzDr9K6O759DxdKZMhWZRm/96d/Q30zEv/mq0ThueHneLRGdRGP93fxcI3O4s8eHeI4/BQAcOn8\nFXz7m98CAFx99w1MlWlv/fs//RM8YcqsebaJlasU9zYT1ODQcYrDYRetNp0TDx7cxc5jchj+7bX/\n+Zlj1CcME5KX4bhJeJBOQd0b6+ynWQLF9S3jwSEEZ00ur66Mq28IiTffJZmdm/cfYe8GGX2u48J3\nOOs6DtFgceq33ngdC3N01yonQMaK+KNPb2HEYqR5AuQc94tcIv8C0gj1Kw14dXpHcTWDqND+qHYM\nwm3OtltPgBqdH9VL0zCCPv+4s4PuE9oTV9+8ht1NolkPdg8Qc/ZlmsfIeC/mUYyI44FduQRP053j\ntXKEa5TBKr0YyTE7NhfOWoNxen4G3RbR3MmwiyFofmqoPHOMpzTfaTttp+20nbbTdtpO2y/RvlRk\nKsuyMV3huDAshxiUq4iTHf5MYjMzNtbXcYazaPxSxVJyxweb8Dl7KihVrReYZzly/kymc4yGZGGW\nSiUbpJ5lGiUOCNVZaoP6dDaW3BdSWpSFgtUnx6beef1NaK415HoKjs/omDP2xpR0IDhbLA+l1XhS\njoOXX6bARaNrCCUFvcZu1WZS6SxByF7MKM8xYORm//gYc+xR5vEQeUaeaa3SRM6V5T/54B6++i4F\nGXqBiyRlxEjBUjvPakJKWz1eOA6CWpn/RyNlLzke9DHqjWu0VblUhl8KIBlyhZAYp0kaIGA6NE2Q\nsVfhuD5KjLaZcgUpR892djaRsqfo+y68WvMfd9QtfSFtqZOtXClDMO24v3+A3X3OzvQdeD55ioNB\niBFrIaXSoFJiJGswtFlg9VoKwWs8NwY5xgHoBXqVJCniuEBhxijo/MLMeC1nuUVAkjQ7UaMrs9lH\naZp9Ic9o1O9gb4u8vSvPX0Scs/iqqlgKtRSUT5QWScfllqREzqKmnuuPkw1AulAA5VEIXvNRHCNk\npFKDdcUAvH7tGs5fIMj+3s3P0Noi0b12u4Ptg4Le9WzGThKnyJLJEwkIYTuR9VlMkNBjLTMlLdol\npLS1vnKTo8blLCQUBItjvveD9/DBTz8AAEzPzqDCKPfxcQcej326OQ3XG9c3LJ4ZxlFRAhHlUmCR\nL51nVtjzq199G+cvXJhofL7rIIwLjToXK0u0zzZ6Oe4+oCD2a9euojFD3v7UVBOlMu3/w8MQIVN+\nU+UELmdUBdUApkZzk5eO8XhEZ2ggm8h4LZebLppMKXaTNcsMlJQDlKk/XnWERpn2etkX8Dyi5KIw\nxuh48gD0XqeDbEh9k6lEPqKfly4u4vCAQgxGyQjRNKEqf3/7FsI2B9BnMQ4OOdtueRGa15HGEe5w\nRt57T7at9qEWLtYekZ7Yy2fr+B/++bcBAF+5dA5/9t7fAQBurN3D7m1KrLh9eIQG66rVl+ZxOKD9\n8eES50BPAAAgAElEQVTthzC9cYLUs9pJ5FkbY7PktDH2jJTCAHy+djodi+J+tPPYUn7bG49x/WMq\nKdZsNiE5s++g04Vk2mthdg46prvk7NwMqswOPbp3F7ubZX5+29KpB9vrGDFF66oyfIfLo0EgSiZH\nptCsgsF7uKlAtsk1B/diDLcIJdTCQ8zrUAY+Pj+kpJVH928j3aPz/vbNzxBzlujR0REMJ7EZ5BA8\nV66jEAtanzVtYEZFgskIPZ9Fa30X4iGXKTraQcyliWoVhRKv26PwGOjR+VerPxuZ+pIV0HNIOeZ9\nU75EAqVsiqaSAmfPUKyFkQ6iHm2M3v4DfPYJCaRBuvjGP/vPaAB+BZovmixJbcxDmiTIePGleQpX\nFcUdc1v00ZgcblEHSWTW8HGVsjRPGEdPKa8/q001q0gKjllIOA7RGzrRkEWWhnYgNPVH5kDAi96T\nPlSvSNNWkJwCH48OIPkwD5IYusYHeB7D4eyWRi1ArUQ/f/7jDzDs7vFzgEuXCOpeOreCzXWCOa+9\n/hKOOpyx4QzglCYzpnobn8H1yXhxIZG3aMEPnRmrEm0GbVQ5Bq40uwzpjMVQi2b00xsx59T/zbsf\no7d9n+bGr2Dppa8BAGYXV+ByLbHm6hnkXIA1i0LETGn4mLIZfFLEyFNOXTILX0yRXRlriOskQZ0v\nTA2g2+lz/yWWF0lcUZgUrqKNORwmKLZVEufIWP4hyjLkvDbzLLMHS5rksCofRloja2d73wrSnVlZ\ntXRhYlIrYy+zzKbUw2Tj2IYJmsxCPLlFMSHvfu0aNNPgYRiiyvRJo9GAy85Pr98/IdgnnxLsE2Nt\nCmv0VcplW6bAYPz7/mCAkA/qUslFZYrG+Mo7V7Fdoe96/OAR7DmtHAgeV5Zl1gmZpPle6akCxYbj\nvMSJgsYQAnFWZPxpO89eyUWJMxbTocZf/sX3AAB/+O//CEdcXDzTKS5coNgYow1CVl1OsxRBmS6d\nJIkRRePajkU2lDEayQnh0ISpeCUVzp9dnmx8bgmdiEMHHIVyhZ5dNzV0+DLP0hSL58kpS+LM1ter\nBBcgM77ERhtwFWfZDkKkDq19FeTwp+iZcwsOohFLbyQ54iHNwagfkkYKAGUSGD6z/LqLPGeqUwFT\nAa0pkZ1Be2fy2nyvf+V1VH1aI2u317B+m84s9EZwWMxVGGB/RA7YB59+jH/9JtFb766uoDlPcUNR\ndQHDQph4McHiZZJMqK0P0OZsxIfrO/gxi3DOLHqos1THt168gndeegkA8L0fvo//4//6PQBA+7CP\nlKn13TCCyzG9qulAVyevW0dFaPkQ0JktOg6RIi/qECuNsleADw6qAa3f6XoNmteRlPIEZZ2hyXJA\nOztbCBM6t6YCH6Njit3VvRY0nzfX8xx1rjRwdHxs9/rC2UtYWaTYvk4vQqvDciGOZzM0J2nlmWn0\nrtNdEccpSiMuMpxoiDKtq5mXZpHO0Xz+7Wfv47t/RnvuYGcLFTay6nNN3L9zl58Toz5P76hcrSLj\nO6TVPbacW6IfwRWchYozeJllWUooIR7QmtFxjgoXshZpgpirtIRpAjFhnUzglOY7bafttJ2203ba\nTttp+6Xal4pMSangMc2zuXYXQ/ZQw2GMpUUK6jvqjBD2yesZHGzi4U8oIDsMI/jzrwEALr/8VRyQ\nUYko76LCJR0c6aDQMqsGZauXk8QZclUE+SYYgyIZPNbOqVXGwpFpmiHhqMokzZ/K8ntWM64DkxWZ\nZhJxn4NYdRmuIm9VGoVAUZ+zOLaZP7mI4BcUiwpQ4q/Nw7ZFCCQMRlz5PTYuRkV9rVodijMlz128\ngnJAnteTtTVUOEvj3HMvQHOphZ9+eA/DkGDyxUtlNJzJgpcf3/0UJZc9vN4eRofU30qkECwThVBa\nOocK02G5FDCMOv5s9sfJgOAtztZ48Ml7aLrkYZQXLlgERGhjqVcpDBwWjXRKZegWBQRHhxsIWOun\nlA2Q5lw6A+YLIVO97hCCt0alXAVYF0dIBb/QRitVUGJaJ421LdPh+yX4TI3JE4HOrhRweY7TKIYn\ni+w8x5Y+UkpBlwvRwy7u3CYP7MrlSyeCoWG1dpRQGJ3Inirq603SGr5Eb5+EXdce3sPZl18FQAH3\nhV6S67qWQjfGWMQKGKNRSo0Du/M8t7SgUspqpiml4PBzPM+zgqujQRdIaB6GnWOETI8uLi3B5aD2\nKMnQOTrgsUsoNXnNs5Olo/I8twHXURIizwh9iePY6kA1GlU4fD4FfgnxiPblH//hd/C7v/t/AiCK\npRjvaBhia4MCjf1SACWLeYhRrxO9VKvVbLkgpRyUOENQa20Rq6mpKaQMxfV6PXR7k2lpZYm2tUVn\nGgtQDu2bRrUJaWg97m12cfEcoTODToTjIy7nFUmYmPpyeNhBWuNafuU+hgcswjnj4cU36D2cWahi\nc58O3Y3dAaI9To4IDRw+H2enanZdDPstSIee47gKkpNd6lMVXHrh/ETjA4Crbz+PDtNMwfTzmF4h\ntEXGCnvHhL7rFDAcZKySEOVkjcbrLuHGLvW53haIh3RuZukI/Zj3EFzEGaNLoxTvf0Jo7coZD1e/\ncY2+N6hiapb6/B//6rcxy3v9R5/+CNfvU2B3N88wfYnusOv7m4hEEf4wQdMaYNSUFmkBVUsYZiRq\nlQDfuEb3X+BkWJ4n5N8vBbjDSM3i0iJKrHsmpUKTxVc77SPcuU9zMlsrwSkTUmniIVwuoyKlsshO\nvbKEaaZN33r36xjE1J/bD9fxo48oiD8XErmZHH1zHQcipTVQ85rI9+jgit0IC9eI6nfPVPFoQPTr\n969/gCfrdD7FoxEaV1hbcaqGfS43F8UxZi7Q7x1XYjAgBGqYjHCk6GdHliAVnSvnOsA3Nun3qumi\neZl03uRBhETz2XP1PNqf0r1YjQxKXAtykvbl1uZrP0bCl8sn730X+5wVU2meQZLRIPudXXQYhkyS\nEWSJDoLq9KuII4Ihb3x+HzU+lKYaTfhMbzWrARosgNmcriJg+NB1XTjFRawyq0SeCAfbe/Rd9+7f\nxfwCTe7i0pKtZeT5AbIvQPOZVKBiuICzriPlYplxIhDxaa4cjVpAz/RzBd/lNFGdWQHKHBJpkaJu\ngAPOnuqnETr8zDAXSIuaUVlq4zSWLjwPxUZidW7Z1pPrdY6w/oBiAg63tzA9R/OZO1OYnZ0sTmPp\nlW+hs04batDag8sp7P3tW1jlzEVn5Q1opjeh3CKkBsJkY2X0Ey3PMuzep0PMdzSWLpBRtnjpNbjz\n9E6Mhp0bHQ9hmM5TfgCfBScHvT6iLsWBqMzAbdKhl+d6nNI7QTve70C49Ey/VIURrNQOA8kXx9tv\nvQHDtO3eziYaFZbhyBNL3yRZRtliIAG+ghlzfBeaKW4BbenlclC2hpWGxiEr/W5t70KyGGnJdRFx\nbbv9jQ3MLtAB7jg+jJx8O5c8BcX02c3rH2HlOZrz3ACdLl3m9XrdZshora1h5XneOLPvxLyejP3I\njbH0Q5ZlVmjU9T0InpNe6wA5OxJpu4eYabKZ+Xk82aCz4fbde1iaZ6G9ZtNmC07SlFKWrtje3sbm\nNu31VAuMOAbRAPBZbPHNN1/DwhxJAehc4z/8yXcAAL/7u79rqxooR9kMwTzN0OPU7DnXwdwM7fvX\nX3sZr75O1Hqt2kReFJTOjY0Z1VrbmoDLS8vwffr59u3btnj1s5pUClVW+x6GBlnKmdKlEtKULsmD\n7XV0D2j9DuMMh0d0aQz6HcT8nsuI4Qqaj7orUMtovv1+CWcqLGZ4kOPmLdr3vdRBMyfay0QOYqaK\nyn4FgsexubuPJldkKFczSIdjOP0cqy9MfkENjw6xw8Wh69PzKM/Rvrx94w5SFh1tVJfQX6fzMaoO\nYS5QfM3G3duYPXuVxiIqWH9CNFPZUVjfo3NirdtGWhjBcYb1J0T5/fVf/RSqTnP760vPIRzSOwlq\nNfza194FAFx96Tz++u/+ip5zsIel18mBfTVcxYPjcV23ZzZjrLCqMoAq6HRtbCzruaVVLDTozput\nupifpTk87A3QqPI9J3IIzbGYcYq9Tepzo1zCNIcqrMxNI+zQ/DheZeygKjU2rJTCzDTL0CQRhi06\nbyq+a8GHYRhjGE5ejSAKexA8z06ljMhwVt2cRDJF66QVdrD9hJyT+bkFXPm1rwIA+lsHqHDYRRhH\naLGzoXWOMON4tyRBlhZVFgyGHOJx1b8Mw6BK182QTvH9uiIx2KH+qyowtUDzMxIhwj36fOP5WThf\nQP7hlOY7bafttJ2203baTttp+yXal4pMDWMfWUoexLV3fws//LvfBwA8efAPFtb3ggqEQ4hJUJrH\nmfOU3ZZHMTLNnpTrI2PLs3N4DMNZW4euD89jmsfzUOEg0ErZQ4NrZS0FCmdy8s6SwMeNhxTs/OFn\nN1EL6Ocr58/ilRfJy3BSiY/ep0wO/He//swxpv0EdfY4Z6eXcQyynIdrO5hjSu7M7AyaM+TxHfY6\n2Dskb6jV7WLA1nU/ExiwUZxkGilDpJmU8JlCawY+muwpdHod9FqsOZXFCPj3g94RYs7CypIY+/sE\nYdab0xgNGFpOG9DxZMhN1ffQ40DOxsIVrLxJcxIOj9DaIbi589P/D7UGa7FcegMBi4NmXs166QCh\nFACwvfYQ23cJmVpenIHD4wtHAxjOVvLcAIa9NM/xLFKTQkBLpmYq04gYuAj3ugiyAhFIYbLJM4gc\nCBimzJQjURb00CRJqEo7AOgUr732In3GhKjyWOI0wX6HvK44zDBkr8j3PUjN2aJaQ7NHZYxAmefH\nUQqGRVhrTsMGbW+sb6F1SJ79/EINdx7cAQB8+MMf4s23SZ/o/EtXkYvJfaMwd5CE9Pzo4X08vkVa\nO1euvmNL8gwHI5ScQv9GI2E0TUk5rmeXaxQZc0oqW+Yi1UDKSQJaSpRKRVarRJ+zIEu+BwcsuFt2\nEHDAfalSxeuvEe3ou4BmDTI/nYLrTk7zua6LWpXXXpbhmHXKoLwTtT2Frfv16MkaRpwxuvbgPr7z\nx38CAOj1+hZxy9LMlpBxpITHFKGABv+I2dkmlpaYQhA+wJo9eW5sgoHjOPY5J3Fvx3EmLl9VDgJ0\nNc3HzdsbODomyqMyZ5AwBZMnBjc/oXerKiX0YtaK6g+RDCxUih5nPDm+g1pRNijX8BhW3nh0jA1G\nC8OGhAva31V/GkrQfj3qjKA92q/CXbGlwMLRDnIuzCYrgHInR4nLtRl8421CTVvtAf7s478FABxs\nH8ErSuNoDxC0ptpRjnvbtOfecg3mGfXvGIOVN+kuybIMPiNHqbgJrcdnQ4fFiz971EL7/6YA6KUz\nl/DGm7TXk2ECmdMzm+UafvvXqH5iGI3QSahDV6Ie5rzJ12kSh3BQJEilSJhuiyVwYZWSEa6+8hxm\navTMiytzMByZ3lMSCyxmXApKFg2eP3sG2zsc/tDvgPWcUQ08xEe8NtIYBVwujIIRhR5ahojrQzpC\nQ/FaciX1FQBaxz0ccTLOJO1gewthiz4/eHKEqWVCgrIZjd1dyuJNTY6QM/WGh8co1zhkoBYg5rJA\nJlU2SSTTKeLhuKZeoZ/rlSWm+ozEOVMAa/FFQYZswEh4KjDipKF+ew+VNieE3RLI+T4Jh33odpHN\n92w09Us1prxyCSrjGkGqhK//y/8RAND+w/8dd29S0c9qrYHFJcoeqFWbKGfEm7Y3NuAyJVCpN9Cc\no0XmNerQrBLrOmXUOayj2nCQClpw68dtfHz3NgBAeSVMczr/pYVp/MpX3wYAvPXOC3BYDDPJcjx+\n9BAA8L0PPsDxcPKIfqVc+GWa+JsP72JnnzbtgltBtUoHrA5jbO6ywFv7CA8PCXbtx6lN5MhzbWuY\nua5jRUdVptFgRfMK+ojadHG3Nh/gyRO6ZPM4stKOVG+M518AhmNOguoMelxc2nE8jAaTGRv9zjFK\ndaJCPEQos+hbUJtCbZoo2eHcE2zfo1iC3gffxdw0Lez6K/8Cbo1iSdI0QgGMPrp1HdsbtKHm56cx\n7HPMiFOCv8C0l2fGsSdwgB5trsSL4PPB4koFzZIZEBqDNtEDyq1CY/J4ImlSSElzXAocSF5fJd+x\ntNHO1hpmeFzCJKhW6J3PBNPos8xABo0BX84mjuAXl2Q2rsUllUJuitpXCsIpxB4lXK6L1mn38OAB\nxQ7WKpcwaNN6maoGON7fsfNvvoBSf26ETa4cdLv4kLOY5pcvYuUKXV7Cc9Bm2nTtwSPM8KE9Oztr\n6wCmUIAtxjpWGYfJbTat0DlyXs2OAAKe27xaRsaZiaWpWcwVhZrzEK5Lh/bKchmf/Jj2bhamNsNx\nkqa1hssxbufOncOAhUD3W22EnD1nzDiD76B1hHuf07r98P0fYdDl2xrGpv+fpA59z0ONM0xdx8E+\nv4ut7Q38yre+yX/qFhntTOuxDIMQ9jmkXk/zmSSJNVSf1TwBCK5GMOwnGPT40mu4UEzZ5DrGgwdE\nz9Vn5iADLrrsVyACcuiOWxoOK5oHlRqUZmMx1+j32DgeKkw5dH7tHTzCpiR5gDlnBU3FhbGlj4xj\nMivVGuDz3yZDHHAGZKwDlIPJs07/+C++h9fOvQwAkKmDrEV/WxV1pBnRWInowWM161Hi432mbw6z\nx3g+oLihgXaxukyhDFXp4ZDP31KpgsTjbNFYo1fE1Y0y9B+QMfL7v/eHaNb+KwBURF5yVQ63MQ/j\nk7FeliWAHaH+4QH+o/NvTzzGOByhUaZneo6L6QXaZ+fPLuMrbxNNOVev4vmLlOV+cWXeygBUWi18\n/Ml1AMDZ1VVsbNB9SWuTDYqlRcwvkMRQniSY4jp0Jo0h2DnxPA8pG/pZnls5j48+/ggZ1wfs5w6y\nrHin2tJqk7Tq7DS8c3Q2//g//B3aO3TGv/0b1+BzDcckTdA+JmBhd+0JDvr0fsu+jxobOHc+v2fj\n8lzHQZ4WMZHSeiV5GmNOkw1RgYORprHMyRo6D+i9Z22FlUtkQyy98zJCNsrEQYxOiaUg5qrwKqe1\n+U7baTttp+20nbbTdtq+lPalIlOD411cvER1k4bhEGfmyVr+X//t/4YPf/xnAIDv/D9/gNu3iC46\nt7KE118nr2TmK9ewy97EUThAt4i+d5p499qb9MzuEO0tCtqtpR50jzxFZ9TBi29QwGTcaKLPiEVt\nOsPjDULEblz/HI/5+cLxYVjgzXFn4da/QD0waTBiYcHDVg93b30CAFgTDh5UySNYOXMOirN9ulmO\no1FRK1CjxnSINpnVz3KVY+spRdEImoN5bz++jeGARfVKCnGPA1dPVCE3gPWqowyYWiJUqdyYhfEZ\nITA59g4nC5icWrmI/Ig8tvh4Hznb43kyhMPIS+PMFdRXyAvsHu7h0YcEl5f/9g9w/i2iBUurr9q+\nr925AXDpn+4oxTTjtUmW2fp9xhjreYRRH8k2URep8qBc1vQpzSDiEjJBGRixCGt4tA/JpSEmab6j\nMeQSP1kawbflHQSShJ45HIZYXyMvcLpRx/QMBaumBoDVUvOskGM4GsCwuFTguBBciqFaqSFlmloo\nB4L7nKWJ1STyfRd7e7SWw3AZDx+R1k631ULEWj6bB+/b7Ln/ZYIxCgFbx9IYg4Nton9b23tYvkD6\nWZA+Drim3l98548sVf7KKy9jfoEoJb8+jRILrvq+jzJT647rQLFHaLRGVtBkcYjBESFrRkgwE4hU\nS5vde7h/D65D6ILnSJQrTLNCImHdrkmakFR6AwCqtTJee5Wo+42NXdx7wNRCnkFyJ8JOHzdv0Lpq\nHx3bQPPAc+C7RV1CaWk4zy1B8Wf8wEOdEyGyVGPYp/UzPTsPjXFSQVHiKktzi4h1Oj2bYZym6cQ0\nH9IUZUasq+WKLQOU5w5KjJQKaRDFHDzfM5gJznDfXQhGVaKohFFEHngYNxFynbvEi3F8THOT9BSq\nGSFTVXOESHFGVamKfkTfa9ImAsEZrkKiy1nHStQRNOk86Ccxcjk5zacFcPcBrXfdTtHeoPUYJyFq\nq3SWzaxeRrdDa6f7aBc7HHCfOTnOBpQgM1tv4O9/+CPqQ6eLmMWOTdnF6IiRMplDFQHHWkEw0vTg\no8/xvd//QwDAb/72r+LMq0T51asNSK6HqV0fDUYXh9u7uPcXPwAAnHnpv5xonItcA/HFyxewyuKr\nczM1S8+NBh3cvkU02cM7t5Fw+EM3GuG4TfT1cBhBcxjC0dGRpaYb9SZyvuqVdFDiMBS/VoXDosiN\nRtPSzvV6DceccPHZ559bkV03d23pKyU9OM7kVGZ1eRbvXf8+AGBXHtpA9us//sAiTb1+HwMOLq+4\nCou8n2rlCrYek8DwcaeDEiPYQmjkhcakyCEF1xzMgLJPKFgDPuqM9O4edDH/65Q5ONNYxGCNbAWn\nobD4r+idiihD85juUTFXgmr6E4/xSzWmHu/sYm2bNsO5+SYeD2hDzi6fxetvUOT++eVFfOf/pYX7\n8Se3sfZX9PmLq+ewOEOTu9T0cdimw/b7P/0A97foOS+9cg0rz3OM1eIMZqrMfW6v48F9ogp+8Ee/\nD4cX/f7eLt79+r8AAHz9G9/G3t9QnbA0ju3FJ0wC/wvEosR6hMSwoXd2Cd2UBORa+7vYbROEaVrb\neOEsXUaXzl3Ekz8iauFwZxcjrqMVDvsYdGlc0zMLqDYIttzf3cV6SptHJz1ErCxdLQdIiwwlgbHC\ns6DYEYBqhhWq87OLy3A6vCG7beT62QqvAOCVa+hwlkhuIgg+wJWGpatEktgLoTGzjKu/8d8CAB59\n9hPc+THVzbo46uGYKd+HDx+gwhmZw9EIGRffVEEdshBbzRKbFTgY9OBzXE+1Pg2+qzBqHyAb0Bz7\ntT5Srv03aA+QDToTjQ8APJEizWiNdFvHCIraeUJBseFQC5qQgn4ul2ooVchYqzamcG2eLqy1x4/x\n2cdkrOdaIhV0sXsGKDOt0qyWEPGB3B1lKKpsZuHAxitBaAxHrMJeCnDhJXIe7t6+gzMv0CEQVBtf\nqG5dpVKxNJNSCi+w8n65UkaecAbfMMajz2nf7Dx5CC5pjY0Hd9Fgum12bh7BCSmIQgJh+cJ5vHKV\n1r4fVCwVmGkB6XOtLNeBxxzY9Q9+gg3eo35pgOcu03pPRgZTXNC2vBA8Jfz6rJYksT2ojTH2AD97\nZgXbnCHWHfSKkCZsPHmM3e0t/tuIVKfB8W62RqG2gr6OktZRqVWrePUVcvyCoGLp3cWlM2h3+/zM\nFAuzc/Y5haBolmY45LhJx3EmpvmUNDBcl81kPXSP6HuC+T5mWHAy6y1CpPTz3t4+PJeMcj/woApi\nQpegM3qfx0cKh/s0N3PRANVpNoiHiY3nlKUSco8uPVHObQzU1oM16B7N2fTqEtwm0+C6bM+jTA9x\nzM7sJG1VncfmId0BRitMnyOqq398jMUa0TSi76B3j+ZP9gxKhi7SSqmMLsfa7G9uYYsLAsdhhCmO\npRvtH0PFTI8awC2yZpXCPN+jqyUHCdeD27x5H2efp0zNPEoggkI2xYfh+ZyeW0TNfzjxGKUAHj2g\nz5dVDhOxBMWDITJWK5eQEBxuUAkqcNmIr03XMMf1QoNKBQ1W0vd9Dz7HcZaCGhTvOUdIuBwWUfIU\ncqb5jB7XEZVSotogx//iiy8i5ZjR2w938Lc/pVASpXxbhHmSduvW5/i9P/53AICzZ1fx6nN0hn3/\ne39j5UuMMci5b3ES2uIF+4eH6LEDpo04oRVqbH1L6QrwlKDklFDz6Uzt9UL4LIothIIaFc5/gimP\nxtju9nCwSePSSNFkMGS0pTGvaY2tzi8+c4ynNN9pO22n7bSdttN22k7bL9G+VGTqoDWyHtvth+s2\n+FR/9MTChyIfwHWoRMMrby9g1CKI98nGBh6usUUdeKgEhdeTIAzJa7j/8BN0u6RT0bif4aMPiWI7\n3DvAzBxB1Mr1rfbPcbeNu4/p+W+88w0sLZKFv7X9GJIRnAyupXYmab3oGFlGHtDZ1edRm3kDALB3\neBZHe+RhIQxR5/o/1167hO/8AXlV7d3b2GHhMa21FX+crpWxu0bz5vqBDeYdDHuIWeuj3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BVMfzzz2HWa4h9+qnPo7vf4/iiLKsgODl0TiBnA9CRkg4HhutWKPgqLJkNMDVd0joMNrt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\n+dqxT1W5mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9264ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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fEZ9ltBz31ZKuAL4J3BcRlwDvALfMHo6ZDWVs8sfI/zTfntr8C+DzwHeb7TuB63qJ0Mx6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9257358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# Your code\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
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